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  1. While Seeq does not officially have dark mode, google chrome has a plug in that may be a feasible workaround. Here is what organizer topic looks like And here is workbench analysis screenshot Interested in getting one for your Seeq? Check out the dark reader plugin. DISCLAIMER: PLEASE CHECK WITH YOUR LOCAL IT BEFORE INSTALLING ANYTHING FROM THE INTERNET Here goes the link: https://chrome.google.com/webstore/detail/dark-reader/eimadpbcbfnmbkopoojfekhnkhdbieeh Cheers, Sanman
    7 points
  2. A small team of us (with help from Seeq team members) built a short script to extract signal names from our legacy excel workbooks so that we could push them to Seeq workbench. Perhaps, like us, you are involved in migrating workbooks for monitoring/ reporting over to Seeq and could do with a boost to get started so you can get to real work of setting up the Seeq workbench. The attached script will extract the signal names (assuming you can craft your own regex search filter) from each excel worksheet and then push them to workbench with labeling for organization. Hopefully its a help to some 🙂 signal_transfer_excel2seeq_rev1.ipynb
    5 points
  3. The SPy Documentation for spy.assets includes an example of specifying metrics as attributes in asset classes. It is also possible to push scorecard metrics using the spy.push functionality by defining the appropriate metadata. An example of this process is given in the code snippets below: #import relevant libraries import pandas as pd from seeq import spy Log in to the SPY module if running locally using spy.login, or skip this step if running Seeq Data Lab. #Search for data that will be used to create the scorecard. This example will search the Example asset tree to find tags in Area A. search_result = spy.search({'Path': 'Example >> Cooling Tower 1 >> Area A'}) The next code segment creates and pushes a signal that will be used as a threshold limit in the scorecard metric. This can be skipped if threshold signals will not be used in the final metric. #Define data frame for low limit threshold signal. my_lo_signal = { 'Type': 'Signal', 'Name': 'Lo Signal', 'Formula': '$signal * 50', 'Formula Parameters': {'$signal': search_result[search_result['Name'] == 'Optimizer']['ID'].iloc[0]} } #Push data frame for low limit threshold signal. lo_push_result = spy.push(metadata=pd.DataFrame([my_lo_signal]), workbook='Example Scorecard') Finally, create the and push the scorecard metric. This example metric measures the average temperature and apply a static high limit threshold of 90 and a moving low limit threshold using the signal defined above. #Define data frame for scorecard metric. my_metric_input = { 'Type': 'Metric', 'Name': 'My Metric', 'Measured Item': {'ID': search_result[search_result['Name'] == 'Temperature']['ID'].iloc[0]}, 'Statistic': 'Average', 'Thresholds': { 'Lo': {'ID': lo_push_result['ID'].iloc[0]}, 'Hi': 90 } } #Push data frame for scorecard metric. spy.push(metadata = pd.DataFrame([my_metric_input]), workbook='Example Scorecard') The final result can be seen in the created workbook.
    5 points
  4. FAQ: How do I put a pump curve in Seeq? As of R21.0.44.00 this can be done the with the scatter-plot tool. This method does require version R21.0.44.00 or newer to work. See the steps below for details. Determine the X&Y components of the curve. This can be done with a tool such as https://apps.automeris.io/wpd/. Enter or paste the components in columns A and B in the CurveFitter excel sheet. See screenshot below for details. The CurveFitter file can be found here. CurveFitter.zip Once the new Flow and Head data has been pasted into excel copy the contents in from D2 to E9 and paste them into the Seeq formula tool. See screenshots below for copy paste details Copy: Paste: Paste the following syntax in the same formula under the coefficients. Be sure that the flow signal has the variable name “$flow”. $f=$flow.remove($flow.isNotBetween($lower,$upper)).setunits('') $coeff4*$f^4+$coeff3*$f^3+$coeff2*$f^2+$coeff1*$f+$const Final formula view: Add the line to the Scatterplot by selecting the f(x) in the Scatterplot tool bar and pick the correct item from the select item dropdown. If adding more than one curve, then click on the item properties “i” of the first curve and click on duplicate. Once in the formula tool copy the new coefficients from excel replacing the old one and hit execute. Follow step 5 to add the curve to the plot. Final View:
    5 points
  5. Hi Mattheus, You can do this by using the move (v49+) or delay (<=v48) function. For example, your equation would be: ($signal.move(5s)+$signal.move(10s)+$signal.move(60s))/3
    5 points
  6. Contextual data is often brought into Seeq to add more information to time series data. This data tends to be brought in as a condition, with the capsule properties of this condition containing different pieces of information. In some cases, a particular capsule property may not contain just one piece of information; it may contain different pieces that are separated based on some logic or code. Rather than having users visually parse the code to extract the segments of interest, Seeq can be used to extract the substring continuously. The code below extracts a substring based on its location in the property. This code is based on incrementing from left to right, starting at the beginning of the string. Changing the inputs will extract a substring from different positions in the property selected. //Inputs Section (Start and end assume reading left to right) $condition = $hex_maint //Recommend to filter condition to only include correct property values $property_to_capture = 'Reason Code' $start_position = 1 //Incrementing starts from 1 $number_of_characters = 2 //Including the start //Code Section $property_signal = $condition.toSignal($property_to_capture).toStep(2wk) //Change duration for interpolation $start_position_regex = ($start_position - 1).toString() //Regular exression indexes from 0 $number_of_characters_regex = ($number_of_characters - 1).toString() $property_signal.replace('/.{'+$start_position_regex+'}(?<Hold>.{'+$number_of_characters_regex+'}.).*/','${Hold}') This alternative version is based on incrementing right to left, starting at the end of the string. //Inputs Section (Start and end assume reading left to right) $condition = $hex_maint //Recommend to filter condition to only include correct property values $property_to_capture = 'Reason Code' $end_position = 1 //Relative to end, incremented from 1 $number_of_characters = 4 //Including the end character //Code Section $property_signal = $condition.toSignal($property_to_capture).toStep(2wk) //Change duration for interpolation $end_position_regex = ($end_position).toString() $number_of_characters_regex = ($number_of_characters - 1).toString() $property_signal.replace('/.*(?<Hold>.{'+$number_of_characters_regex+'}.{'+$end_position_regex+'})$/','${Hold}') Note the output of these formulas is a string. In the case that a numeric value is wanted, append .toNumber() after '${Hold}') Below is an example of the results. With this substring parsed, all of Seeq's analytical tools can be further leveraged. Some examples are developing histograms based on the values of the substring and making conditions to highlight whenever a particular value in the substring is occurring.
    5 points
  7. Users are often interested in creating pareto charts using conditions they've created in Seeq sorted by a particular capsule property. The chart below was created using the Histogram tool in Seeq Workbench. For more information on how to create Histograms that look like this, check out this article on creating and using capsule properties. Often times users would like to see the histogram above, but with the bars sorted from largest to smallest in a traditional pareto chart. Users can easily create paretos from Seeq conditions using Seeq Data Lab. A preview of the chart that we can create is: The full Jupyter Notebook documentation of this workflow (including output) can be found in the attached pdf file. If you're unable to download the PDF, the code snippets below can be run in Seeq Data Lab to produce the chart above. #Import relevant libraries from seeq import spy import pandas as pd import numpy as np import matplotlib import matplotlib.pyplot as plt Log in to the SPY module if running locally using spy.login, or skip this step if running Seeq Data Lab. #Search for your condition that has capsule properties using spy.search #Use the 'scoped to' argument to search for items only in a particular workbook. If the item is global, no 'scoped to' argument is necessary condition = spy.search({ "Name": "Production Loss Events (with Capsule Properties)", "Scoped To": "9E50F449-A6A1-4BCB-830A-8D0878C8C925", }) condition #pull the data from the time frame of interest using spy.pull into a Pandas dataframe called 'my_data' my_data = spy.pull(condition, start='2019-01-15 12:00AM', end='2019-07-15 12:00AM', header='Name',grid=None) #remove columns from the my_data dataframe that will not be used in creation of the pareto/CDF my_data = my_data.drop(['Condition','Capsule Is Uncertain','Source Unique Id'], axis=1, inplace=False) #Calculate a new dataframe column named 'Duration' by subtracting the capsule start from the capsule end time my_data['Duration'] = my_data['Capsule End']-my_data['Capsule Start'] #Group the dataframe by reason code my_data_by_reason_code = my_data.groupby('Reason Code') #check out what the new data frame grouped by reason code looks like my_data_by_reason_code.head() #sum total time broken down by reason code and sort from greatest to least total_time_by_reason_code['Total_Time_by_Reason_Code'] = my_data_by_reason_code['Duration'].sum().sort_values(ascending=False) total_time_by_reason_code['Total_Time_by_Reason_Code'] = total_time_by_reason_code['Total_Time_by_Reason_Code'].rename('Total_Time_by_Reason_Code') total_time_by_reason_code['Total_Time_by_Reason_Code'] #plot pareto of total time by reason code total_time_by_reason_code['Total_Time_by_Reason_Code'].plot(kind='bar') #Calculate the total time from all reason codes total_time = total_time_by_reason_code['Total_Time_by_Reason_Code'].sum() total_time #calculate percentatge of total time from each individual reason code percent_time_by_reason_code['Percent_Time_by_Reason_Code'] = total_time_by_reason_code['Total_Time_by_Reason_Code'].divide(total_time) percent_time_by_reason_code['Percent_Time_by_Reason_Code'] #Calculate cumulative sum of percentage of time for each reason code cum_percent_time_by_reason_code['Cum_Percent_Time_by_Reason_Code'] = percent_time_by_reason_code['Percent_Time_by_Reason_Code'].cumsum() cum_percent_time_by_reason_code['Cum_Percent_Time_by_Reason_Code'] = cum_percent_time_by_reason_code['Cum_Percent_Time_by_Reason_Code'].rename('Cum_Percent_Time_by_Reason_Code') cum_percent_time_by_reason_code['Cum_Percent_Time_by_Reason_Code'] #plot cumulative distribution function of time spent by reason code cum_percent_time_by_reason_code['Cum_Percent_Time_by_Reason_Code'].plot(linestyle='-', linewidth=3,marker='o',markersize=15, color='b') #convert time units on total time by reason code column from default (nanoseconds) to hours total_time_by_reason_code['Total_Time_by_Reason_Code'] = total_time_by_reason_code['Total_Time_by_Reason_Code'].dt.total_seconds()/(60*60) #build dataframe for final overlaid chart df_for_chart = pd.concat([total_time_by_reason_code['Total_Time_by_Reason_Code'], cum_percent_time_by_reason_code['Cum_Percent_Time_by_Reason_Code']], axis=1) df_for_chart #create figure with overlaid Pareto + CDF plt.figure(figsize=(20,12)) ax = df_for_chart['Total_Time_by_Reason_Code'].plot(kind='bar',ylim=(0,800),style='ggplot',fontsize=12) ax.set_ylabel('Total Hours by Reason Code',fontsize=14) ax.set_title('Downtime Reason Code Pareto',fontsize=16) ax2 = df_for_chart['Cum_Percent_Time_by_Reason_Code'].plot(secondary_y=['Cum_Percent_Time_by_Reason_Code'],linestyle='-', linewidth=3,marker='o',markersize=15, color='b') ax2.set_ylabel('Cumulative Frequency',fontsize=14) plt.show()
    4 points
  8. When creating signal forecasts, especially for cyclic signals that degrade, we often use forecastLinear() in formula to easily forecast a signal out into the future to determine when a threshold is met. The methodology is often the same regardless of if we are looking at a filter, a heat exchanger, or any other equipment that fouls overtime or any equipment that needs to go through some periodic maintenance when a KPI threshold is met. A question that comes up occasionally from users is how to create a signal forecast that only uses data from the current operation cycle for signal forecasting. The forecastlinear() operator only takes into account a historical training period and does not determine if that data is coming from the current cycle or not (which results in unexpected results). Before entering the formula, you will need to define: a condition that identifies the current cycle, here i have called it "$runningCycle" a Signal to do a linear forecast on, i have called it "$signal" To forecast out into the future based on only the most recent cycle, the following code snippet can be used in formula: $training = $runningCycle.setmaximumduration(10d).toGroup(capsule(now()-2h, now())) $forecast=$Signal.predict($training, timesince(toTime('2000-01-01T00:00Z'))) $signal.forecastSplice($forecast, 1d) In this code snippet, there are a few parameters that you might want to change: .setMaximumDuration(10d): results in a longest cycle duration of 10 days, this should be changed to be longer than the longest cycle you would expect to see capsule(now-2h, now()): this creates a period during which seeq will look for the most recent cycle. In this case it is any time in the last 2 hours. If you have very frequent data (data comes in every few seconds to minutes) then 2 hours or less will work. If you have infrequent data (data that comes in once a day or less) then extend this so that it covers the last 3-4 data points. $signal.forecastSplice($forecast, 1d): When using forecastLinear(), there is an option to force the prediction through the last sample point. This date parameter (1 day in this case) does something similar- it blends the last historical data point with the forecast over the given time range. In other words, if the last data point was a value of 5, but my first forecasted datapoint had a value of 10, this parameter is the time frame over which to smooth from the historical data point to the forecast. Here is a screenshot of my formula : and the formula in action:
    4 points
  9. Capsules can have properties or information attached to the event. By default, all capsules contain information on time context such as the capsule’s start time, end time, and duration. However, one can assign additional capsule properties based on other signals’ values during the capsules. This guide will walk through some common formulas that can be used to assign capsule properties and work with those properties. Note: The formula syntax used in the following examples will all be based on the formula language for Seeq version 49. If you have questions about errors you may be receiving in the formulas on different versions, please check out the What’s New in Seeq Knowledge Base pages for formula changes or drop a comment below with the error message. How can I visualize capsule properties? Capsule Properties can be added to the Capsules Pane in the bottom right hand corner of Seeq Workbench with the black grid icon. Any capsule properties beyond start, end, duration, and similarity that are created with the formulas that follow or come in automatically through the datasource connection can be found by clicking the “Add Column” option and selecting the desired property in the modal to get a tabular view of the properties for each capsule as shown below. In Trend View, you can Capsule Property labels inside the capsules by selecting the property from the labels modal. Note that only Capsule Properties added to the Capsule Pane in the bottom right corner will be available in the labels modal. In Capsule Time, the signals can be colored by Capsule Properties by turning on the coloring to rainbow or gradient. The selection for which property is performing the coloring is done by sorting by the capsule property in the Capsule Pane in the bottom right corner. Therefore, if Batch ID is sorted by as selected below, the legend shown on the chart will show the Batch ID values. When working with Scorecard Metrics, you can use a Capsule Property as the header of a condition based scorecard by typing in the property name into the header modal: How do I create a capsule for every (unique) value of a signal? Let’s say you have a signal that you want to turn into individual capsules for each value or unique value of the signal. This is often used for string signals (e.g. batch IDs, operations, or phases) that may look like the signal below. There’s two main operators that can be used for this: $signal.toCapsules() The toCapsules operator will create a capsule for each data point of the signal. Therefore, if there was only 1 data point per value in the string signal below, it would create one capsule per value, but if the string value was recorded every minute regardless of whether it change values, it would create 1 minute capsules. In addition, the toCapsules operator also automatically records a Capsule Property called ‘Value’ that contains the value of the signal data point. $signal.toCondition('Property Name') The toCondition operator will create a capsule for each change in value of the signal. Therefore, in the case above where the value was recorded every minute regardless of value changes, it would only create one capsule for the entire time the value was equivalent. Similarly to the toCapsules operator, the toCondition operator also automatically records a Capsule Property called ‘Value’ that contains the value of the signal data point. However, with the toCondition operator, there’s an optional entry to store the property under a different name instead by specifying a property name in the parentheses in single quotes as shown in the example above. Note: Sometimes when working with string signals of phases or steps that are just numbered (e.g. Phase 1), if there is only one phase in the operation, you may end up wanting two Phase 1 capsules in a row (e.g. Operation 1 Phase 1 and Operation 2 Phase 1) whereas the toCondition method above will only create a single capsule. In this instance, it can be useful to concatenate the operation and phase signals together to find the unique combination of Operations and Phases. This can be done by using the following formula: ($operationsignal + ': ' + $phasesignal).toCondition('Property Name') How do I assign a capsule property? Option 1: Assigning a constant value to all capsules within a condition $condition.setProperty('Property Name', 'Property Value') Note that it is important to know whether you would like the property stored as a string or numeric value. If the desired property value is a string, make sure that the ‘Property Value’ is in single quotes to represent a string like the above formula. If the desired value is numeric, you should not use the single quotes. Option 2: Assigning a property based on another signal value during the capsule For these operations, you will have to use a transform operator to perform a particular set of operations per capsule to retrieve the desired property. For example, you may want the first value of a signal within a capsule or the average value of a signal during the capsule. Below are some examples of options you have and how you would set these values to capsule properties. The general format for this operation is listed below where we will define some different options to input for Property Scalar Value. $condition.transform($capsule -> $capsule.setProperty('Property Name', Property Scalar Value)) The following are options for what to input into Property Scalar Value in the formula above to obtain the desired property values: First value of signal within a capsule: $signal.toScalars($capsule).first() Last value of signal within a capsule: $signal.toScalars($capsule).last() Average value of signal within a capsule: $signal.average($capsule) Maximum value of signal within a capsule: $signal.maxValue($capsule) Minimum value of signal within a capsule: $signal.minValue($capsule) Standard deviation of signal within a capsule: $signal.stdDev($capsule) Totalization of a signal within a capsule: $signal.totalized($capsule) Count the capsules of a separate condition within a capsule: $DifferentCondition.count($capsule) Duration of capsules in seconds of a separate condition within a capsule: $DifferentCondition.totalduration($capsule) There are more statistical operations that can be done if desired, but hopefully this gives you an idea of the syntax. Please leave a comment if you struggle with a particular operator that you are trying to perform. Finally, there are often times when you want to perform one of the above operations, but only within a subset of each capsule. For example, maybe for each batch, you want to store the max temperature during just a particular phase of the batch. In order to do this, first make sure you have created a condition for that phase of the batch and then you can use the following to input into Property Scalar Value in the formula above: $signal.within($PhaseCondition).maxValue($capsule) In this case, the within function is cutting the signal to only be present during the $PhaseCondition so that only that section of the signal is present when finding the maximum value. Option 3: Assign a property based on a parent or child condition In batch processing, there is often a parent/child relationship of conditions in an S88 (or ISA-88) tree hierarchy where the batch is made up of smaller operations, which is then made up of smaller phases. Some events databases may only set properties on particular capsules within that hierarchy, but you may want to move the properties to higher or lower levels of that hierarchy. This formula will allow you to assign the desired property to the condition without the property: $ConditionWithoutProperty.transform($capsule -> $capsule.setProperty('Current Property Name', $ConditionWithProperty.toGroup($capsule).first().property('Desired Property Name'))) Note that this same formula works whether the condition with the property is the parent or child in this relationship. I also want to point out that if there are multiple capsules of the $ConditionWithProperty within any capsule of the $ConditionWithoutProperty, that this formula is set up to take the property from the first capsule within that time span. If you would like a different capsule to be taken, you can switch the first() operator in the formula above to last() or pick(Number) where last will take the property from the last capsule in the time span and pick is used to specify a particular capsule to take the property from (e.g. 2nd capsule or 2nd to last capsule). There’s another write-up about this use case here for more details and some visuals: How do I filter a condition by capsule properties? Conditions are filtered by capsule properties using the keep operator. Some examples of this are listed below: Option 1: Keep exact match to property $condition.keep('Property Name', isEqualTo('Property Value')) Note that it is important to know whether the property is stored as a string or numeric value. If the property value is a string, make sure that the ‘Property Value’ is in single quotes to represent a string like the above formula. If the value is numeric, you should not use the single quotes. Option 2: Keep regular expression string match $condition.keep('Property Name', isMatch('B*')) $condition.keep('Property Name', isNotMatch('B*')) You can specify to keep either matches or not matches to partial string signals. In the above formulas, I’m specifying to either keep all capsules where the capsule property starts with a B or in the second equation, the ones that do not start with a B. If you need additional information on regular expressions, please see our Knowledge Base article here: https://support.seeq.com/space/KB/146637020/Regex%20Searches Option 3: Other keep operators for numeric properties Using the same format of Option 1 above, you can replace the isEqualTo operator with any of the following operators for comparison functions on numeric properties: isGreaterThan isGreaterThanOrEqualTo isLessThan isLessThanOrEqualTo isBetween isNotBetween isNotEqualTo Option 4: Keep capsules where capsule property exists $condition.keep('Property Name', isValid()) In this case, any capsules that have a value for the property specified will be retained, but all capsules without a value for the specified property will be removed from the condition. How do I turn a capsule property into a signal? A capsule property can be turned into a signal by using the toSignal operator. The properties can be placed at either the start timestamp of the capsule (startKey) or the end timestamp of the capsule (endKey): $condition.toSignal('Property Name', startKey()) $condition.toSignal('Property Name', endKey()) This will create a discrete signal where the value is at the selected timestamp for each capsule in the condition. If you would like to turn the discrete signal into a continuous signal connecting the data points, you can do so by adding a toStep or toLinear operator at the end to either add step or linear interpolation to the signal. Inside the parentheses for the interpolation operators, you will need to add a maximum interpolation time that represent the maximum time distance between points that you would want to interpolate. For example, a desired linear interpolation of capsules may look like the following equation: $condition.toSignal('Property Name', startKey()).toLinear(40h) It should be noted that some properties that are numeric may be stored as string properties instead of numeric, particularly if the capsules are a direct connection to a datasource. In this case, a .toNumber() operator may need to be added after the toSignal, but before the interpolation operator. Finally, it is often useful to have the property across the entire duration of the capsule if there are no overlapping capsules (e.g. when looking at batches on a particular unit). This is done by turning the signal into a step signal and then filtering the data to only when it is within a capsule: $condition.toSignal('Property Name', startKey()).toStep(40h).within($condition) What capsule adjustment/combination formulas retain capsule properties? When adjusting or combining conditions, the rule of thumb is that operators that have a 1 to 1 relationship between input capsule and output capsule will retain capsule properties, but when there are multiple capsules that are required as the input to the formula operator, the capsule properties are not retained. For example, moving a capsule by 1 hour has knowledge of the input properties whereas merging 2 capsules together results in not knowing which capsule to keep the properties from. A full list of these operators and their stance on whether they retain or lose capsule properties during usage is below. Operators that retain properties Operators that lose properties afterEnd inverse afterStart merge beforeEnd fragment beforeStart intersect ends join starts union (if more than one capsule overlap) middles grow growEnd shrink move combineWith encloses inside subtract matchesWith touches union (when no capsules overlap) It is important to note that capsule properties are attached to the individual capsule. Therefore, using a combination formula like combinewith where multiple conditions are combined may result in empty values in your Capsule Pane table if each of the conditions being combined has different capsule properties. How do I rename capsule properties? Properties can be swapped to new names by using the renameProperty operator: $condition.renameProperty('Current Property Name', 'New Property Name') A complex example of using capsule properties: What if you had upstream and downstream processes where the Batch ID (or other property) could link the data between an upstream and downstream capsule that do not touch and are not in a specific order? In this case, you would want to be able to search a particular range of time for a matching property value and to transfer another property between the capsules. This can be explained with the following equation: $UpstreamCondition.move(0,7d) .transform($capsule ->{ $matchingDownstreamProperty = ($DownstreamCondition.toSignal('Downstream Property to Match Name') == $capsule.property('Upstream Property to Match Name')) $firstMatchKey = $matchingDownstreamProperty.togroup($capsule,CAPSULEBOUNDARY.INTERSECT).first().startkey() $capsule.setProperty('Desired Property Name',$DownstreamCondition.toSignal('Desired Downstream Property Name').tostep(40h).valueAt($firstMatchKey)) }) .move(0,-7d) Let's walk through what this is doing step by step. First, it's important to start with the condition that you want to add the property to, in this case the upstream condition. Then we move the condition by a certain amount to be able to find the matching capsule value. In this case, we are moving just the end of each capsule 7 days into the future to search for a matching property value and then at the end of the formula, we move the end of the capsule back 7 days to return the capsule to its original state. After moving the capsules, we start the transform. Inside the transform, we find the matching downstream property by turning the capsule property to match from the downstream condition into a signal and match it to the capsule property from the upstream capsule. We then find the key (timestamp) of the first match of the property. Again, similar to some other things, the first option could be swapped out with last or pick to grab a different matching capsule property. Finally, we set the property on the upstream condition to 'Desired Property Name' by turning the downstream capsule property that we want into a step signal and take the value where the first match was found.
    4 points
  10. It is common in manufacturing processing plants such as oil and gas refineries, to monitor the temperature trend in furnaces. Example in the refineries, the furnaces tube metal temperature (TMT) monitoring severity increases for dirty services such as crude distillation and coker units. Operation team uses this information to decide either to go for rate cut or feed rate skewing before the TMT reaching mechanical limits to prolong the run length. Upon reaching the limit, the furnace will be taken out-of-service by means of spalling or pigging, and consequently impacts the production rate. Use case: The objective is to highlight the highest and the second highest temperature out of several temperatures in a matrix. Seeq enables users to build a matrix table (or Scorecard prior to R51) to highlight the temperature priority sequence by using a combination of functions and tools including max(), splice(), composite condition and scorecard metrics. Step 1: Start by loading all of the signals we want to include in the matrix into the display. Step 2: Use max() to look for the highest value signal at any time in the formula tool. Type in formula below into formula editor. $t.max($t2).max($t3).max($t4).max($t5).max($t6).max($t7).max($t8) Step 3: Create the second highest signal using splice() and composite condition. To capture the second highest signal, we need first to exclude a signal with the highest temperature at any time and then identify the highest value out of the remaining seven signals. To achieve that, use the highest temperature signal we created in step 2, we then create a condition when a signal reads the highest value, for each eight signals. //Which is the max $if_t1_is_the_max = $t1 == $max $if_t2_is_the_max = $t2 == $max $if_t3_is_the_max = $t3 == $max $if_t4_is_the_max = $t4 == $max $if_t5_is_the_max = $t5 == $max $if_t6_is_the_max = $t6 == $max $if_t7_is_the_max = $t7 == $max $if_t8_is_the_max = $t8 == $max Prior to looking for the max a second time, we must remove or replace the values from each of the signals when they are equal to the max. In this method, we will replace the highest signal values with zero using the splice function during the condition when that signal was the max. With these highest values replaced by zero (or removed), applying the same technique with the max function will yield the value of the second highest signal. //replace the max with 0 $removing_the_max_value = ($t1.splice(0,$if_t1_is_the_max)) .max($t2.splice(0,$if_t2_is_the_max)) .max($t3.splice(0,$if_t3_is_the_max)) .max($t4.splice(0,$if_t4_is_the_max)) .max($t5.splice(0,$if_t5_is_the_max)) .max($t6.splice(0,$if_t6_is_the_max)) .max($t7.splice(0,$if_t7_is_the_max)) .max($t8.splice(0,$if_t8_is_the_max)) .toStep() return $removing_the_max_value Step 4: Create Metric Threshold Limits Subtract the highest signal by a fairly small value using Formula tool in order to use signal as a threshold limit. Repeat the step for second highest limit. $max-0.001 Step 5: Create scorecard metric for each signal. Create scorecards for all 8 signals, as an example we choose value at the end for statistic for daily condition and apply the threshold accordingly. In the table view: Do check this post by Nick. He used different approach to yield the maximum of three signals, and displayed signal string in a matrix table.
    4 points
  11. For those Formula-savvy users, a one Formula approach to this would be as follows where you would define your percentage of the capsule in the first line: $percent = 10%.tosignal().resample(1s) $condition.transform($capsule ->{ $movetime = $capsule.duration()*$percent.toScalars($capsule).first() capsule($capsule.startKey(),$capsule.startKey()+$movetime)})
    4 points
  12. Monitoring KPI's for your process or equipment is a valuable method in determining overall system performance and health. However, it can be cumbersome to comb through all the different KPI's and understand when each is deviating from an expected range or set of boundaries. We can, however, shorten our time to insight by aggregating all associated KPI's into one Health Score; the result allows us to monitor just one trend item, and take action when deviations occur. To walk through the steps of building a Health Score, I will walk through an example below which looks at 4 KPI's for a Pump, and aggregates them into one final Health Score. Note that the time period examined is a 3 month period leading up to a pump failure. KPI DETAILS KPI #1 The first indicator I can monitor on this pump is how my Discharge Pressure is trending relative to an expected range determined by my Manufacturer's Pump Performance Curve. (To enable using a pump curve in Seeq, reference this article for more information: Creating Pump and Compressor Curves in Seeq). As my Discharge Pressure deviates from the expected range, red Capsules are created by using a Deviation Search in Seeq. KPI #2 The second indicator I can monitor on this pump is whether the NPSHa (available) is remaining higher than the NPSHr (required) as stipulated by the Manufacturer's Pump Datasheet. If my NPSHa drops below my NPSHr, red Capsules will be created by using a Deviation Search in Seeq. (No deviations noted in the time period evaluated). KPI #3 The third indicator I can monitor on this pump is whether the pump Vibration signals remain lower than specified thresholds (these could be determine empirically, from the Manufacturer, or industry standard). In this case I have 4 Vibration signals. I am using a "Union" method to combine the 4 conditions into the final KPI Alert, which will show red Capsules if any of the 4 vibrations exceed their threshold. The formula for this KPI Alert condition is as shown below: $vib1>$limit1 or $vib2>$limit2 or $vib3>$limit3 or $vib4>$limit4 KPI #4 The fourth indicator I can monitor on this pump is whether the flow through the pump is remaining higher than minimum allowable as stipulated by the Manufacturer's Pump Curve/Datasheet. If my measured Flow drops below my Flow Limit, red Capsules will be created by using a Deviation Search in Seeq. (No deviations noted in the time period evaluated). BUILDING THE HEALTH SCORE Now that we have 4 conditions, 1 for each KPI if exceeding the determined normal operating range, we need to aggregate these into the Health Score. First, we determine how much % time each KPI alert has been active during each Day (in fraction form, ie range of 0 to 1). We do this by creating a Formula for each KPI Alert condition, with the syntax as follows: #Determine the % duration that a KPI alert is active during each Day (in fraction form) $kpi1.aggregate(percentDuration(), days(), startKey()).convertUnits('').toStep() The result of applying this to all 4 KPI Alert conditions should be as follows - you may note that if a KPI Alert condition is "on" for the full duration of a day, it will show a value of 1. If partially "on", it will show a fractional value between 0 and 1, and if no Condition is present at all, it will show a value of 0. Now we aggregate these individual indicators into a rolled up Health Score, by using the Sum of Squares method, and then dividing by the total number of indicators. To do so, enter the following in a formula: #Aggregate the Sum of Squares of the fractional alert values ($k1.pow(2) + $k2.pow(2) + $k3.pow(2) + $k4.pow(2))/4 I could also have performed the above 2 steps in 1 Formula: #First determine the % duration that a KPI alert is active during each Day (in fraction form) $k1 = $kpi1.aggregate(percentDuration(), days(), startKey()).convertUnits('').toStep() $k2 = $kpi2.aggregate(percentDuration(), days(), startKey()).convertUnits('').toStep() $k3 = $kpi3.aggregate(percentDuration(), days(), startKey()).convertUnits('').toStep() $k4 = $kpi4.aggregate(percentDuration(), days(), startKey()).convertUnits('').toStep() #Then aggregate the Sum of Squares of those fractional values $sumOfSquares = $k1.pow(2) + $k2.pow(2) + $k3.pow(2) + $k4.pow(2) $sumOfSquares/4 I can also add a Health Score high limit (in my example 0.25 = if one KPI Alert is active for a full day), to trigger some action (perhaps schedule pump maintenance), prior to failure. A new red Capsule will appear if my Health Score exceeds this limit of 0.25 (can be configured via Value Search or Deviation Search). Enter the following into Formula to create this limit (a new scalar): Below you will see my final Health Score trend item as well as the limit and Health Score Alert condition. Optionally, I can use the High Limit to create a shaded boundary for my Health Score, using the Boundaries Tool. (I also create a low limit of 0 to be the lower boundary). We can see that in the month leading up to Failure (early Feb), I had multiple forewarning indications through this aggregated Health Score. In future, I could monitor this Pump health in a dashboard in a Seeq Organizer Topic, and trigger some maintenance activity proactively. Dashboard Example:
    4 points
  13. Various parts of the world display date and time stamps differently. Often times, we get requests for changing the order of month and day in the timestamp string or to display the date as a Scorecard metric in a specific format. This can be done using the replace() operator in Formula. For example, let's say we wanted to pull the start time for each capsule in a condition and display it as mm/dd/yyyy hh:mm format: $condition.transformToSamples($cap -> Sample($cap.getStart(),$cap.getProperty('Start')), 1d) .replace('/(?<year>....)-(?<month>..)-(?<day>..)T(?<hour>..):(?<minute>..):(?<sec>..)(?<dec>.*)Z/' , '${month}/${day}/${year} ${hour}:${minute}') This takes the original timestamp (for example: '2019-11-13T17:04:13.7220714157Z') and parses it into the year, month, day, hour, minute, second, and decimal to be able to set up any format desired. The various parts of the string can then be called in the second half of the replace to get the desired format as shown above with ${month}/${day}/${year} ${hour}:${minute}. From there, you can either view this data in the trend or use Scorecard Metric to display the Value at Start in a condition based metric. If the end time is desired instead of the start, the only changes needed would be to (1) switch the .getStart operator to .getEnd, and (2) switch the .getProperty('Start') to .getProperty('End'). Note: The '1d' at the end of the 2nd line of the formula represents the maximum interpolation for the data, which is important if you want to view this as a string signal. This value may need to be increased depending on the prevalence of the capsules in the condition.
    4 points
  14. When addressing a business problem with analytics, we should always start by asking ourselves 4 key questions: Why are we talking about this: what is the business problem we are trying to address, and what value will solving this problem generate? What data do I have available to help with solving this problem? How can I build an effective analysis to identify the root of my problem (both in the past, and in future)? How will I visualize the outputs to ensure proactive action to prevent the problem from manifesting? This is where you extract the value. With that in mind, please read below how we approach the above 4 questions while working in Seeq to deal with heat exchanger performance issues. What is the business problem? Issues with heat exchanger performance can lead to downstream operational issues which may lead to lost production and revenue. To effectively monitor the exchanger, a case-specific approach is required depending on the performance driver: Fouling in the exchanger is limiting heat transfer, requiring further heating/cooling downstream Fouling in the exchanger is limiting system hydraulics, causing flow restrictions or other concerns Equipment integrity, identify leaks inside the exchanger What Data do we have available? Process Sensors – flow rates, temperatures, pressures, control valve positions Design Data – drawings, datasheets Maintenance Data – previous repairs or cleaning, mean-time between cleanings How can we tackle the business problem with the available data? There are many ways to monitor a heat exchanger's performance, and the selection of the appropriate indicator depends on a) the main driver for monitoring and b) the available data. The decision tree below is merely meant to guide what indicators can be applied based on your dataset. Generally speaking, the more data available, the more robust an analysis you can create (ie. first principles based calculations). However, in the real world, we are often working with sparse datasets, and therefore may need to rely on data-based approaches to identify subtle trends which indicate changes in performance over time. Implementing each of the indicators listed above follow a similar process in Seeq Workbench, as outlined in the steps below. In this example, we focus on a data-based approach (middle category above). For an example of a first-principles based approach, check out this Seeq University video. Step 1 - Gather Data In a new Workbench, search in the Data Tab for relevant process signals Use Formula to add scalars or use the .toSignal() function to convert supplemental data such as boundary limits or design values Use Formula, Value Search or Custom Condition to enter maintenance period(s) and heat exchanger cycle(s) conditions (if these cannot be imported from a datasource) Step 2 - Identify Periods of Interest •Use Value Search, Custom Condition, Composite Condition or Formula to identify downtime periods, periods where exchanger is bypassed, or periods of bad data which should be ignored in the analysis Step 3 - Cleanse Data Use Formula to remove periods of bad data or downtime from the process signals, using functions such as $signal.remove($condition) or $signal.removeOutliers() Use Formula to smooth data as needed, using functions such as $signal.agileFilter() or the Low Pass Filter tool Step 4 - Quantify Use Formula to calculate any required equations In this example, no calculations are required. Step 5 - Model & Predict Use Prediction and choose a process signal to be the Target Variable, and use other available process signals as Input Variables; choose a Training Period when it is known the exchanger is in good condition Using Boundaries: establish an upper and lower boundary signal based on the predicted (model) signal from previous step (e.g. +/-5% of the modeled signal represents the boundaries) Step 6 - Monitor Use Deviation Search or Value Search to find periods where the target signal exceeds a boundary(ies) The deviation capsules created represent areas where heat exchanger performance is not as expected Aggregate the Total Duration or Percent Duration statistic using Scorecard or Signal From Condition to assess deteriorating exchanger health over time How can we visualize the outputs to ensure proactive action in future? Step 7 - Publish Once the analysis has been built in a Seeq Workbench, it can be published in a monitoring dashboard in Seeq Organizer as seen in the examples below. This dashboard can then be shared among colleagues in the organization, with the ability to monitor the exchanger, and log alerts and take action as necessary as time progresses - this last step is key to implementing a sustainable workflow to ensure full value is extracted from solving your business problem.
    4 points
  15. Sometimes users want to find more documentation on the SPy functions than what is provided in the SPy Documentation Notebooks. A quick way to access SPy object documentation from your notebook is by using the Shift + Tab shortcut to access the docstring documentation of the function. Example view of the docstrings after using the Shift + Tab shortcut: You can expand the docstrings to view more details by clicking the + button circled in red in the above image. Expanded docstrings: From here, you can scroll through the documentation in the pop-up window. Another useful shortcut is the Tab shortcut. This will show a list of available functions or methods for either the SPy module or any other python object you have in memory. Example view of the Tab shortcut:
    4 points
  16. Hi Theresa, no, you cannot adjust the size of the lanes itself.But you can use the "Dimming" feature of Seeq to show only lanes with items selected in the Details Pane: Regards, Thorsten
    4 points
  17. Hi Esther, you can do this the following way: 1. Create a Periodic Condition (found in Tools - Pane): 2. Use Signal from Condition to calculate the total duration. Result: Regards, Thorsten
    4 points
  18. To better understand their process, users often want to compare time-series signals in a dimension other than time. For example, seeing how the temperature within a reactor changes as a function of distance. Seeq is built to compare data against time but this method highlights how we can use time to mimic an alternate dimension. Step 1: Sample Alignment In order to accurately mimic the alternate dimension, the samples to be included in each profile must occur at the same time. This can be achieved through a couple methods in Seeq if the samples don't already align. Option 1: Re-sampling Re-sampling selects points along a signal at select intervals. You can also re-sample based on another signal's keys. Since its possible for there not to be a sample at that select interval, the interpolated value is chosen. An example Formula demonstrating how to use the function is shown below. //Function to resample a signal $signal.resample(5sec) Option 2: Average Aggregation Aggregating allows users to determine the average of a signal over a given period of time and then place this average at a specific point within that period. Signal From condition can be used to find the average over a period and place this average at a specific timestamp within the period. In the example below, the sample is placed at the start but alignment will occur if the samples are placed at the middle or end as well. Step 2: Delay Samples In Formula, apply a delay to the samples of the signal that represents their value in the alternative dimension. For example, if a signal occurs at 6 feet from the start of a reactor, delay it by 6. If there is not a signal with a 0 value in the alternate dimension, the final graph will be offset by the smallest value in the alternate dimension. To fix this, in Formula create a placeholder signal such as 0 and ensure its samples align with the other samples using the code listed below. This placeholder would serve as a signal delayed by 0, meaning it would have a value of 0 in the alternate dimension. //Substitute Period_of_Time_for_Alignment with the period used above for aligning your samples 0.toSignal(Period_of_Time_for_Alignment) Note: Choosing the unit of the delay depends upon the new sampling frequency of your aligned signals as well as the largest value you will have in the alternative dimension. For example, if your samples occur every 5 minutes, you should choose a unit where your maximum delay is not greater than 5 minutes. Please refer to the table below for selecting units Largest Value in Alternate Dimension Highest Possible Delay Unit 23 Hour, Hour (24 Hour Clock) 59 Minute 99 Centisecond 999 Millisecond Step 3: Develop Sample Profiles Use the Formula listed below to create a new signal that joins the samples from your separate signals into a new signal. Replace "Max_Interpolation" with a number large enough to connect the samples within a profile, but small enough to not connect the separate profiles. For example, if the signals were re-sampled every 5 minutes but the largest delay applied was 60 seconds, any value below 4 minutes would work for the Max_Interpolation. This is meant to ensure the last sample within a profile does not interpolate to the first sample of the next profile. //Make signals into discrete to only get raw samples, and then use combineWith and toLinear to combine the signals while maintaining their uniqueness combineWith($signal1.toDiscrete() , $signal2.toDiscrete() , $signal3.toDiscrete()).toLinear(Max_Interpolation) Step 4: Condition Highlighting Profiles Create a condition in Formula for each instance of this new signal using the formula below. The isValid() function was introduced in Seeq version 44. For versions 41 to 43, you can use .valueSearch(isValid()). Versions prior to 41 can use .validityCapsules() //Develop capsule highlighting the profile to leverage other views based on capsules to compare profiles $sample_profiles.isValid() Step 5: Comparing Profiles Now with a condition highlighting each profile, Seeq views built around conditions can be used. Chain View can be used to compare the profiles side by side while Capsule View can overlay these profiles. Since we delayed our samples before, we are able to look at their relative times and use that to represent the alternate dimension. Further Applications With these profiles now available in Seeq, all of the tools available in Seeq can be used to gain more insight from these examples. Below are a few examples. Comparing profiles against a golden profile Determine at what value in the alternate dimension does each profile reach a threshold Developing a soft sensor based on another sensor and a calibration curve profile
    4 points
  19. The replace operator can be applied a signal without a transform. Using the replace function without the transform will be less computationally expensive, resulting in better performance. Both ways will get to the same answer. Syntax for replace function without transform: $signal.replace('/(\\d+)_\\d+_\\w+/', '$1')
    4 points
  20. Hi Pablo, you can use transform() and replace() to do this. I made an example with a signal that contains the following data: To create a signal that contains only the numeric values I used the following formula: $originalSignal.transform(($p, $c) -> sample($c.getKey(), $c.getValue().replace('/\\w+\\s(\\d+)/', '$1'))) Transform is used to access every sample in the signal by specifying a lambda expression. The current sample is temporarily stored in the variable $c. $p contains the previous sample which is not used here. For each sample of the original signal a new sample is created by using the timestamp (key) of the original sample. The value for the new sample is determined by the value of the sample of the original signal on which replace() is executed. As the name suggests replace() is used to replace portions of a string. In this case I make use of a regular expression to determine the numeric value inside the original string and replace the original string by the determined value. The result looks like this: For your example (12345_20200326_PJR) you have to modify the replace part to: .replace('/(\\d+)_\\d+_\\w+/', '$1') Hope this helps. Regards, Thorsten
    4 points
  21. Hi Banderson, you can create a duration signal from each capsule in a condition, using "signal from condition" tool. As you may know these point and click tools create a Seeq formula underneath. So after using point and click signal from condition tool, you can find the syntax of formula in item properties of that calculation. You can copy this syntax and paste it in Formula and use it to further develop your calculations.
    4 points
  22. For those like me who keep getting this error: Error getting data: condition must have a maximum duration. Consider using removeLongerThan() to apply a maximum duration. Click the wrench icon to add a Maximum Capsule duration. The resolution seemed to make sense to apply removeLongerThan() or setMaximumDuration() to the signal, but the correct answer is to set it to the capsule. For example, this is the incorrect formula I attempted. $series.aggregate(maxValue(), $capsules, endKey(), 0s) Here is the resolution: $series.aggregate(maxValue(), $capsules.setMaximumDuration(40h), endKey(), 0s) or $series.aggregate(maxValue(), $capsules.removeLongerThan(40h), endKey(), 0s) Hope this helps others who didn't have luck searching this specific alarm previously.
    4 points
  23. Webhooks are a convenient method to send event data from Seeq to “channel” productivity tools such as Microsoft Teams or Slack. The following post describes how Seeq users can leverage Seeq Data Lab to send messages directly to MS Teams via Webhooks. Pre-Requisites: 1) Seeq Data Lab with Scheduled Notebooks enabled a. See Administration Panel -> Configuration and filter for “Features/DataLab/ScheduledNotebooks/Enabled” 2) MS Teams Channel with a Webhook Connector Assumptions: 1) Summary of capsules generated in a defined time range (i.e., every 12 or 24 hours) 2) Notifications are not near-real-time – script will run on a pre-defined schedule generally measured in hours, not minutes or seconds 3) Events of interest are contained in an Asset Tree or Group with one or more Conditions Step 1: Configure Webhook in MS Teams To send Seeq capsules/events to MS Teams, a Webhook for the target channel needs to be created. Detailed instructions on how to configure Webhooks in MS Teams can be found here: https://learn.microsoft.com/en-us/microsoftteams/platform/webhooks-and-connectors/how-to/add-incoming-webhook For the purpose of this post, we will create a Webhook URL in our “Seeq Notifications” Team to alert on Temperature Excursions. The alerts will be posted in the “Cooling Tower Temperature Monitoring” channel. Teams and Channel names can be configured to fit your need/operation, this is just an example for demonstration purposes: MS Teams will generate a Webhook URL which we will use in our script in Step 4. Step 2: Identify or Create an Asset Group or Asset Tree to define the Monitoring Scope To scope the events of interest, we will use an Asset Tree that contains “High Temperature” conditions for a collection of Monitoring Assets. While this is not a requirement for using Webhooks, it helps with scaling the Notification workflow. It also allows us to combine multiple Conditions from different Assets into a single workflow. To learn how create an Asset Tree, follow the “Asset Trees 1 – Introduction.ipynb” tutorial in the SPy Documentation folder contained in each new Seeq Datalab project. The script for the Monitoring Asset Tree used in this post is attached for reference: Monitoring Asset Tree.ipynb Alternatively, Asset Groups can be also used to create an asset structure directly in Workbench without using Python: Once the Asset Group/Tree containing the monitoring Conditions is determined, create a Worksheet with a Treemap or Table overview for monitoring use: Make note of the URL as it will be included in the Notification as a link to the monitoring overview whenever an event is detected. For locally scoped Asset Groups or Trees, it will also inform the script where to look for Conditions. Step 3: Install the “pymsteams” library in Seeq Datalab The pymsteams library allows users to compose and post messages (or cards) to MS Teams. The library can be installed from the pypi repository (pypi.org) using the “pip install” command. 1) Open a Seeq Datalab Project 2) Launch a Terminal session 3) Install the pymsteams library by executing pip install pymsteams Additional documentation on pymsteams can be found here: https://pypi.org/project/pymsteams/ Step 4: Create or Update the Monitoring script We are now ready configure a monitoring script that sends notifications to the Webhook configured in Step 1 using Conditions scoped to the Asset Tree in Step 2. a) Import the relevant libraries, including the newly installed pymsteams library import pandas as pd from datetime import datetime,timedelta import pytz import pymsteams b) Configure Input Parameters #Refer to Microsoft Documentation on how to configure a Webhook for a MS Teams channel webhook_url='YOUR WEBHOOK HERE' #Specify the monitoring workbook - this is where the alert will link with the associated timeframe monitoring_workbook_url='YOUR WORKBOOK HERE' #Specify the asset tree and associated condition for which the webhook should be triggered asset_tree='Compressor Monitoring' monitoring_condition='High Temperature' #Specify the lookback period and timezone to search for capsules lookback_interval_hours=24 timezone=('US/Mountain') c) Search for Event Capsules #Set time range to look for new conditions delta=timedelta(hours=lookback_interval_hours) end=datetime.now(tz=pytz.timezone(timezone)) start=end-delta #Parse the workbook information workbook_id=spy.utils.get_workbook_id_from_url(monitoring_workbook_url) worksheet_id=spy.utils.get_worksheet_id_from_url(monitoring_workbook_url) #This block is optional, it stores search results for the conditions once instead of searching each time the #script runs. Saves time if the search result is not expected to change. To reset, just delete the .pkl file. pkl_file_name=asset_tree+'_'+monitoring_condition+'_'+workbook_id+'.pkl' try: monitoring_conditions=pd.read_pickle(pkl_file_name) except: monitoring_conditions=spy.search({'Name':monitoring_condition, 'Type':'Condition', 'Path':asset_tree}, workbook=workbook_id,quiet=True) monitoring_conditions.to_pickle(pkl_file_name) #Pull capsules present during the specified time range events=spy.pull(monitoring_conditions,start=start,end=end,group_by=['Asset'],header='Asset',quiet=True) number_of_events=len(events) events d) Send Message to Webhook using the pymsteams library if a Capsule is detected in the time range #If capsules are present, trigger the webhook to compile and send a card to MS Teams if number_of_events != 0: events.sort_values(by='Condition',inplace=True) #Create url for specific notification time-frame using Seeq URL builder investigate_start=start.astimezone(pytz.utc).strftime('%Y-%m-%dT%H:%M:%SZ') investigate_end=end.astimezone(pytz.utc).strftime('%Y-%m-%dT%H:%M:%SZ') investigate_url=f"https://explore.seeq.com/workbook/builder?startFresh=false"\ f"&workbookName={workbook_id}"\ f"&worksheetName={worksheet_id}"\ f"&displayStartTime={investigate_start}"\ f"&displayEndTime={investigate_end}"\ f"&expandedAsset={asset_tree}" #Create message information to be posted in channel assets=[] text=[] for event in events.itertuples(): assets.append(event.Condition) #Capsule started before lookback window if pd.isnull(event[2]): if pd.isnull(event[3]) or event[4] == True: text.append(f'Event was already in progress at {start.strftime("%Y-%m-%d %H:%M:%S %Z")} and is in Progress') else: text.append(f'Event was already in progress at {start.strftime("%Y-%m-%d %H:%M:%S %Z")} and ended {event[3].strftime("%Y-%m-%d at %H:%M:%S %Z")}') #Capsule started during lookback window else: if pd.isnull(event[3]) or event[4] == True: text.append(f'Event started {event[2].strftime("%Y-%m-%d at %H:%M:%S %Z")} and is in Progress') else: text.append(f'Event started {event[2].strftime("%Y-%m-%d at %H:%M:%S %Z")} and ended {event[3].strftime("%Y-%m-%d at %H:%M:%S %Z")}') message='\n'.join(text) #Create MS Teams Card - see pymsteams documentation for details TeamsMessage = pymsteams.connectorcard(webhook_url) TeamsMessage.title(monitoring_condition+" Event Detected") TeamsMessage.text(monitoring_condition+' triggered in '+asset_tree+f' Asset Tree in the last {lookback_interval_hours} hours') TeamsMessageSection=pymsteams.cardsection() for i,value in enumerate(text): TeamsMessageSection.addFact(assets[i],value) TeamsMessage.addSection(TeamsMessageSection) TeamsMessage.addLinkButton('Investigate in Workbench',investigate_url) TeamsMessage.send() Step 5: Test the Script Execute the script ensuring at least one “High Temperature” capsule is present in the lookback duration. The events dataframe in step 4. c) will list capsules that were detected. If no capsules are present, adjust the lookback duration. If at least one capsule is detected, a notification will automatically be posted in the channel for which the Webhook has been configured: Step 6: Schedule Script to run on a specified Frequency If the script operates as desired, configure a schedule for it to run automatically. #Optional - schedule the above script to run on a regular interval spy.jobs.schedule(f'every day at 6am') The script will run on the specified interval and post a summary of “High Temperature” capsules/events that occur during the lookback period directly to the MS Teams channel. Refer to the spy.jobs.ipynb notebook in the “SPy Documentation” folder for additional information on scheduling options. Attached is a copy of the full example script: Seeq MS Teams Notification Webhook - Example Script.ipynb
    3 points
  24. Check out the data lab script and video that walks through it to automate data pull->apply ml->push results to workbench in an efficient manner. Of course you can skin the cat many different ways however this gives a good way to do it in bulk. Use case details: Apply ML on Temperature signals across the whole Example Asset Tree on a weekly basis. For your case, you can build you own asset tree and filter the relevant attributes instead of Temperature and set spy.jobs.schedule frequency to whatever works for you. Let me know if there are any unanswered questions in my post or demo. Happy to update as needed. apply_ml_at_scale.mp4 Apply ML on Asset Tree Rev0.ipynb
    3 points
  25. Have you ever wanted to scale calculations in Seeq across different assets without having to delve into external systems or write code to generate asset structures? Is your process data historian a giant pool of tags which you need to have organized and named in a human readable format? Do you want to take advantage of Seeq features such as Asset Swapping and Treemaps, but do not have an existing Asset structure to leverage? If the answer is yes, Asset Groups can help! Beginning in Seeq version R52 Asset Groups were added to configure collections of items such as Equipment, Operating Lines, KPIs, etc via a simple point-and-click tool. Users can leverage Asset Groups to easily organize and scale their analyses directly in Workbench, as well as apply Seeq Asset-centric tools such as Treemaps and Tables across Assets. What is an Asset Group? An Asset Group is a collection of assets (listed in rows) and associated parameters called “Attributes” (listed in columns). If your assets share common parameters, Asset Groups can be a great way to organize and scale analyses instead of re-creating each analysis separately. Assets can be anything users want them to be. It could be a piece of equipment, geographical region, business unit, KPI, etc. Asset Groups serve to organize and map associated parameters (Attributes) for each Asset in the group. Each Asset can have one or several Attributes mapped to it. Attributes are parameters that are common to all the assets and are mapped to tags from one or many data sources. Examples of Asset/Attribute combinations include: Asset Attribute(s) Pump Suction Pressure, Discharge Pressure, Flow, Curve ID, Specific Gravity Heat Exchanger Cold Inlet T, Cold Outlet T, Hot Inlet T, Hot Outlet T, Surface Area Production Line Active Alarms, Widgets per Hour, % of time in Spec It’s very important to configure the name of the common Attribute to be the same for all Assets, even if the underlying tag or datasource is not. Using standard nomenclature for Attributes (Columns) enables Seeq to later compare and seamlessly “swap” between assets without having to worry about the underlying tag name or calculation. Do This: Do Not Do This: How to Configure Asset Groups in Seeq Let’s create an Asset Group to organize a few process tags from different locations. While Asset Groups support pre-existing data tree structures (such as OSI PI Asset Framework), the following example will assume the tags to not be structured and added manually from a pool of existing process tags. NOTE: Asset Groups require an Asset Group license. For versions prior to R54, they also have to be enabled in the Seeq Administrator Configuration page. Contact your Seeq Administrator for details. 1) In the “Data” tab, create a new Asset Group: 2) Specify Asset Group name and add Assets You can rename the assets by clicking on the respective name in the first column. In this case, we'll define Locations 1-3. 3) Map the source tags a. Rename “Column 1” by clicking on the text and entering a new name b. Click on the (+) icon to bring up the search window and add the tag corresponding to each asset. You can use wildcards and/or regular expressions to narrow your search. c. Repeat mapping of the tags for the other assets until there’s a green checkmark in each row d. Additional source tags can be used by clicking on “Add Column” button in the toolbar In this case, we will add a column for Relative Humidity and map a tag for each of the Locations 4) Save the Asset Group 5) Trend using the newly created Asset Group The newly created Asset Group will now be available in the Data pane and can be used for navigation and trending a. Navigate to “Location 1” and add the items to the display pane by clicking on them. You can also change the display range to 7 days to show a bit more data b. Notice the Assets Column now listed in the Details pane showing from which Asset the Signal originates We can also add the Asset Path to the Display pane by clicking on Labels and checking the desired display configuration settings (Name, Unit of Measure, etc). c. Swap to Location 2 (or 3) using the Asset Swapping functionality. In the Data tab, navigate up one level in the Asset Group, then click the Swap icon ( ) to swap the display items from a different location . Notice how Seeq will automatically swap the display items 6) Create a “High Temperature” Condition Calculations configured from Asset Group Items will “follow” that asset, which can help in scaling analyses. Let’s create a “High Temperature” condition. a. Using “Tools -> Identify -> Value Search” create a condition when the Temperature exceeds 100 b. Click “Execute” to generate the Condition c. Notice the condition has been generated and is automatically affiliated with the Asset from which the Signals were selected d. Swap to a different Asset and notice the “High Temperature” Condition will swap using the same condition criteria but with the signals from the swapped Asset Note: Calculations can also be configured in the Asset Group directly, which can be advantageous if different condition criteria need to be defined for each asset. This topic will be covered in Part 2 of this series. 7) Create a Treemap Asset Groups enables users to combine monitoring across assets using Seeq’s Treemap functionality. a. Set up a Treemap for the Assets in the Group by switching to the Treemap view in the Seeq Workbench toolbar. b. Click on the color picker for the “High Temperature” condition to select a color to display when that condition is active in the given time range. (if you have more than one Condition in the Details pane, repeat this step for each Condition) c. A Treemap is generated for each Asset in the Asset Group. Signal statistics can optionally be added by configuring the “Statistics” field in the toolbar. Your tree map may differ depending on the source signal and time range selected. The tree map will change color if the configured Condition triggers during the time period selected. This covers the basics for Asset Groups. Please check out Part 2 on how to configure calculations in Asset Groups and add them directly to the Hierarchy.
    3 points
  26. Users of OSIsoft Asset Framework often want to filter elements and attributes based on the AF Templates they were built on. At this time though, the spy.search command in Seeq Data Lab only filters based on the properties Type, Name, Description, Path, Asset, Datasource Class, Datasource ID, Datasource Name, Data ID, Cache Enabled, and Scoped To. This post discusses a way in which we can still filter elements and/or attributes based on AF Template. Step 1: Retrieve all elements in the AF Database The code below will return all assets in an AF Database that are based on a AF Template whose name contains Location. asset_search = spy.search({"Path":"Example-AF", "Type":"Asset"}, all_properties=True) #Make sure to include all properties since this will also return the AF Template asset_search.dropna(subset=['Template'], inplace=True) # Remove assets not based on a template since we can't filter with NaN values asset_search_location = asset_search[asset_search['Template'].str.contains('Location')] # Apply filter to only consider Location AF Template assets Step 2: Find all relevant attributes This code will retrieve the desired attributes. Note wildcards and regular expression can be used to find multiple attributes. signal_search = spy.search({"Path":"Example-AF", "Type":"Signal", "Name":"Compressor Power"}) #Find desired attributes Step 3: Filter attributes based on if they come from an element from the desired AF template Last step cross references the signals returned with the desired elements. This is done by looking at their paths. # Define a function to recreate paths, items directly beneath the database asset don't have a Path def path_merger(row): row = row.dropna() return ' >> '.join(row) asset_search_location['Full Path'] = asset_search_location[['Path', 'Asset', 'Name']].apply(lambda row: path_merger(row),axis=1) # Create path for the asset that includes its name signal_search['Parent Path'] = signal_search[['Path', 'Asset']].apply(lambda row: path_merger(row),axis=1) # Create path for the parents of the signals signal_search_location = signal_search[signal_search['Parent Path'].isin((asset_search_location['Full Path']))] # Cross reference parent path in signals with full paths in assets to see if these signals are children of the desired elements
    3 points
  27. Statistical Process Control (SPC) can give production teams a uniform way to view and interpret data to improve processes and identify and prevent production issues. Control charts and run rule conditions can be created in Seeq to monitor near-real time data and flag when the data indicates abnormal or out of control behavior. Creating a Control Chart 1. Find the signal of interest and a signal that can be used to detect the current production grade (ideally a grade code or similar signal - if this does not exist for your product, you can use process set points to stitch together a calculated grade signal). Use .tocondition() in formula to create a condition for each change in the Grade_Code signal. 2. Check data for normalcy and other statistical assumptions prior to proceeding. To check for normalcy, use the Histogram tool in Seeq. Find more information on the Histogram tool in the Seeq Knowledge Base. Note that for this analysis we are using a subgroup size of one and assuming normalcy. 3. Determine the methodology to use to create the average and standard deviation for each grade. In this case, we will identify periods after start-up when the process was in control using Manual Condition, and select these times across each grade. 4. Calculate the mean and standard deviation for each grade, based on the times the process was in-control. Choose a time window that captures all capsules created for the in-control period. To use an unweighted mean, use .toDiscrete() before calculating average. The same calculation for the mean can be used for the standard deviation, by replacing average() with stddev(). //Define the time period that contains all in control capsules $capsule = capsule('2020-10-16T00:00-00:00', '2021-09-21T00:00-00:00') //Narrow down data to when process is in control, use keep() to filter the condition by the specific grade code capsule property $g101 = $allgrades.keep('Grade Code',isMatch('Grade 101')).intersect($inControl) $g102 = $allgrades.keep('Grade Code',isMatch('Grade 102')).intersect($inControl) $g103 = $allgrades.keep('Grade Code',isMatch('Grade 103')).intersect($inControl) //Create average based on the times the product is in control, use .toDiscrete to create an unweighted average $g101_ave = $viscosity.remove(not $g101).toDiscrete().average($capsule) $g102_ave = $viscosity.remove(not $g102).toDiscrete().average($capsule) $g103_ave = $viscosity.remove(not $g103).toDiscrete().average($capsule) //Create average for all grades in one signal using splice(), use keep() to filter the condition by the specific grade code capsule property //use within() to show only average only during the condition 0.splice($g101_ave, $allgrades.keep('Grade Code',isMatch('Grade 101'))) .splice($g102_ave, $allgrades.keep('Grade Code',isMatch('Grade 102'))) .splice($g103_ave, $allgrades.keep('Grade Code',isMatch('Grade 103'))) .within($allgrades) 5. Use the mean and standard deviation to create +/- 1 sigma limits, +/-2 sigma limits, and +/-3sigma limits (sometimes called upper and lower control limits). Here is an example of creating the +2 sigma limit: //Add 2*standard deviation to the mean to create the $plus2sd limit, use within() to show limits only during the time periods of interest ($mean + (2*$standardDeviation)).within($grade_code) 6. Overlay the standard deviation limits and mean with the signal of interest by placing on one lane and one y-axis, remove standard deviation. Creating Run Rules Once the control chart is created, run rule conditions can be created to detect instability and the presence of assignable cause in the process. In this example, Western Electric Run Rules are used, but other run rules can be applied using similar principles. Western Electric Run Rules: Run Rule 1: Any single data point falls outside the 3sigma-limit from the centerline. Run Rule 2: Two out of three consecutive points fall beyond the 2sigma-limit, on the same side of the centerline. Run Rule 3: Four out of five consecutive points fall beyond the 1sigma-limit, on the same side of the centerline. Run Rule 4: NINE consecutive points fall on the same side of the centerline. 7. The following formulas can be used to create a condition for each run rule: Run Rule 1: //convert to a step signal $signalStep = $signal.toStep() //find when one data point goes outside the plus3sigma or minus 3sigma limits ($signalStep < $minus3sd or $signalStep > $plus3sd) //set the property on the condition .setProperty('Run Rule', 'Run Rule 1') Run Rule 2: *Note that the function toCapsulesByCount() is available in Seeq versions R54+ //Create step-interpolated signal to keep from capturing the linear interpolation between sample points $signalStep = $signal.toStep() //create capsules for every 3 samples ($toCapsulesbyCount) and for every sample ($toCapsules) $toCapsulesbyCount = $signalStep.toCapsulesByCount(3,3*$maxinterp) //set the maximum interpolation based on the longest time you would expect between samples $toCapsules = $signalStep.toCapsules() //Create condition for when the signal is not between +/-2 sigma limits //separate upper and lower to capture when the rule violations occur on the same side of the centerline $condLess = ($signalStep <= $minus2sd) $condGreater = ($signalStep >= $plus2sd) //within 3 data points ($toCapsulesByCount), count how many sample points are not between +/-2 sigma limits $countLess = $signal.todiscrete().remove(not $condLess).aggregate(count(),$toCapsulesbyCount,durationKey()) $countGreater = $signal.todiscrete() .remove(not $condGreater).aggregate(count(),$toCapsulesbyCount,durationKey()) //Find when 2+ out of 3 are outside of +/-2 sigma limits //by setting the count as a property on $toCapsulesByCount and keeping only capsules greater than or equal to 2 $RR5below = $toCapsulesbyCount.setProperty('Run Rule 5 Violations', $countLess, endvalue()) .keep('Run Rule 5 Violations', isGreaterThanOrEqualto(2)) $RR5above = $toCapsulesbyCount.setProperty('Run Rule 5 Violations', $countGreater, endvalue()) .keep('Run Rule 5 Violations', isGreaterThanOrEqualto(2)) //Find every sample point capsule that touches a run rule violation capsule //Combine upper and lower into one condition and use merge to combine overlapping capsules and to remove properties $toCapsules.touches($RR5below or $RR5above).merge(true) .setProperty('Run Rule', 'Run Rule 2') Run Rule 3: *Note that the function toCapsulesByCount() is available in Seeq versions R54+ //Create step-interpolated signal to keep from capturing the linear interpolation between sample points $signalStep = $signal.toStep() //create capsules for every 5 samples ($toCapsulesbyCount) and for every sample ($toCapsules) $toCapsulesbyCount = $signalStep.toCapsulesByCount(5,5*$maxinterp) //set the maximum interpolation based on the longest time you would expect between samples $toCapsules = $signalStep.toCapsules() //Create condition for when the signal is not between +/-1 sigma limits //separate upper and lower to capture when the rule violations occur on the same side of the centerline $condLess = ($signalStep <= $minus1sd) $condGreater = ($signalStep >= $plus1sd) //within 5 data points ($toCapsulesByCount), count how many sample points ($toCapsules) are not between +/-1 sigma limits $countLess = $signal.toDiscrete().remove(not $condLess).aggregate(count(),$toCapsulesbyCount, durationkey()) $countGreater = $signal.toDiscrete() .remove(not $condGreater).aggregate(count(),$toCapsulesbyCount,durationkey()) //Find when 4+ out of 5 are outside of +/-1 sigma limits //by setting the count as a property on $toCapsulesByCount and keeping only capsules greater than or equal to 4 $RR6below = $toCapsulesbyCount.setProperty('Run Rule 6 Violations', $countLess, endvalue()) .keep('Run Rule 6 Violations', isGreaterThanOrEqualto(4)) $RR6above = $toCapsulesbyCount.setProperty('Run Rule 6 Violations', $countGreater, endvalue()) .keep('Run Rule 6 Violations', isGreaterThanOrEqualto(4)) //Find every sample point capsule ($toCapsules) that touches a run rule violation capsule //Combine upper and lower into one condition and use merge to combine overlapping capsules and to remove properties $toCapsules.touches($RR6below or $RR6above).merge(true) .setproperty('Run Rule', 'Run Rule 3') Run Rule 4: //Create step-interpolated signal to keep from capturing the linear interpolation between sample points $signalStep = $signal.toStep() //create capsules for every 9 samples ($toCapsulesbyCount) and for every sample ($toCapsules) $toCapsulesbyCount = $signalStep.toCapsulesByCount(9,9*$maxinterp) //set the maximum interpolation based on the longest time you would expect between samples $toCapsules = $signalStep.toCapsules() //Create condition for when the signal is either greater than or less than the mean //separate upper and lower to capture when the rule violations occur on the same side of the centerline $condLess = $signalStep.isLessThan($mean) $condGreater = $signalStep.isGreaterThan($mean) //Find when the last 9 samples are fuly within the greater than or less than the mean //use merge to combine overlapping capsules and remove properties $toCapsules.touches(combinewith($toCapsulesbyCount.inside($condLess), $toCapsulesbyCount.inside($condGreater))).merge(true) .setproperty('Run Rule', 'Run Rule 4') **To make it easier to use these run rules in Seeq, custom formula functions can be created for each run rule using the User Defined Formula Function Editor Add-on which can be found in Seeq’s Open Source Gallery along with user guides and instructions for installation. For example, Run Rule 2 can be simplified to the following formula using the User-Defined Formula Functions Add-on with Seeq Data Lab: $signal.WesternElectric_runRule2($minus2sd, $plus2sd) 9. If desired, all run rules can be combined into one condition in formula using .combinewith(): combineWith($runRule1,$runRule2,$runRule3, $runRule4) 10. As a final step, a table can be created detailing the run rule violations in the trend view. Here, the header column is set as ‘Capsule Property’ >> ‘Run Rule’ and the capsule properties, start, end, and duration were added as columns. The last value of the signal ‘Grade_Code’ was also added as a column to the table. For more information on Tables, see the Seeq Knowledge Base.
    3 points
  28. The following steps will create a prediction model for every capsule in a condition. Step 1. pick a condition with capsules that isolate the desired area of regression. Any condition with non-overlapping capsules will work as long as there are enough sample points within its duration. For this example, an increasing temperature condition will be used. However, periodic conditions and value search conditions will work as well. Step 2. Create a time counter for each capsule in the condition. This can be done with the new timesince() function in the formula tool. The timesince() function will have samples spaced depending on the selected period so it is important to select a period that has enough points to build a model with. See below for details on the timesince() formula setup. Step 3. In this step a condition with capsule properties that hold the regression constants will be made. This will be done in the formula tool with one formula. The concept behind the formula below is to split the condition from step one into individual capsules and use each of the capsules as the training window for a regression model. Once the regression model is done for one capsule the coefficients of the model are assigned as properties to the capsule used for the training window. The formula syntax for a linear model-based condition can be seen below. An example of a polynomial regression model can be found in the post below. $Condtition.removeLongerThan(24h).transform($cap-> { $model=$SignalToModel.validValues().regressionModelOLS( group($cap),false,$Time) $cap.setProperty('Slope',$model.get('coefficient1')) .setProperty('Intercept',$model.get('intercept'))}) Below is a screenshot of how the formula looks in Seeq. Note: The regression constants can be added to the capsule pane by clicking on the black stats button and selecting add column. Below is a screen shot of the results. Step 4. Once a condition with the regression coefficients has been created the information will need to be extracted to a signal form. The following formula syntax will extract the information. This will need to be repeated for every constant from your regression model. e.g.(So for a linear model this must be done for both the slope and for the intercept.) The formula syntax for extracting the regression coefficients can be seen below. $signal=$Condition.transformToSamples( $cap -> sample($cap.getmiddle(), $cap.getProperty('Intercept').toNumber()), 1min) $signal.aggregate(average(),$Condition,durationKey()) Below is a screenshot of the formula in Seeq. Below is a screenshot of the display window of how the signals should look. Step 5. Use the formula tool to plot the equation. See screenshot below for details. Final Result
    3 points
  29. We often get asked how to use the various API endpoints via the python SDK so I thought it would be helpful to write a guide on how to use the API/SDK in Seeq Data Lab. As some background, Seeq is built on a REST API that enables all the interactions in the software. Whenever you are trending data, using a Tool, creating an Organizer Topic, or any of the other various things you can do in the Seeq software, the software is making API calls to perform the tasks you are asking for. From Seeq Data Lab, you can use the python SDK to interact with the API endpoints in the same way as users do in the interface, but through a coding environment. Whenever users want to use the python SDK to interact with API endpoints, I recommend opening the API Reference via the hamburger menu in the upper right hand corner of Seeq: This will open a page that will show you all the different sections of the API with various operations beneath them. For some orientation, there are blue GET operations, green POST operations, and red DELETE operations. Although these may be obvious, the GET operations are used to retrieve information from Seeq, but are not making any changes - for instance, you may want to know what the dependencies of a Formula are so you might GET the item's dependencies with GET/items/{id}/dependencies. The POST operations are used to create or change something in Seeq - as an example, you may create a new workbook with the POST/workbooks endpoint. And finally, the DELETE operations are used to archive something in Seeq - for instance, deleting a user would use the DELETE/users/{id} endpoint. Each operation endpoint has model example values for the inputs or outputs in yellow boxes, along with any required or optional parameters that need to be filled in and then a "Try it out!" button to execute the operation. For example, if I wanted to get the item information for the item with the ID "95644F20-BD68-4DFC-9C15-E4E1D262369C" (if you don't know where to get the ID, you can either use spy.search in python or use Item Properties: https://seeq.atlassian.net/wiki/spaces/KB/pages/141623511/Item+Properties) , I could do the following: Using the API Reference provides a nice easy way to see what the inputs are and what format they have to be in. As an example, if I wanted to post a new property to an item, you can see that there is a very specific syntax format required as specified in the Model on the right hand side below: I typically recommend testing your syntax and operation in the API Reference to ensure that it has the effect that you are hoping to achieve with your script before moving into python to program that function. How do I code the API Reference operations into Python? Once you know what API endpoint you want to use and the format for the inputs, you can move into python to code that using the python SDK. The python SDK comes with the seeq package that is loaded by default in Seeq Data Lab or can be installed for your Seeq version from pypi if not using Seeq Data Lab (see https://pypi.org/project/seeq/). Therefore, to import the sdk, you can simply do the following command: from seeq import sdk Once you've done that, you will see that if you start typing sdk. and hit "tab" after the period, it will show you all the possible commands underneath the SDK. Generally the first thing you are looking for is the ones that end in "Api" and there should be one for each section observed in the API Reference that we will need to login to using "spy.client". If I want to use the Items API, then I would first want to login using the following command: items_api = sdk.ItemsApi(spy.client) Using the same trick as mentioned above with "tab" after "items_api." will provide a list of the possible functions that can be performed on the ItemsApi: While the python functions don't have the exact same names as the operations in the API Reference, it should hopefully be clear which python function corresponds to the API endpoint. For example, if I want to get the item information, I would use "get_item_and_all_properties". Similar to the "tab" trick mentioned above, you can use "shift+tab" with any function to get the Documentation for that function: Opening the documentation fully with the "^" icon shows that this function has two possible parameters, id and callback where the callback is optional, but the id is required, similar to what we saw in the API Reference above. Therefore, in order to execute this command in python, I can simply add the ID parameter (as a string as denoted by "str" in the documentation) by using the following command: items_api.get_item_and_all_properties(id='95644F20-BD68-4DFC-9C15-E4E1D262369C') In this case, because I executed a "GET" function, I return all the information about the item that I requested: This same approach can be used for any of the API endpoints that you desire to work with. How do I use the information output from the API endpoint? Oftentimes, GET endpoints are used to retrieve a piece of information to use it in another function later on. From the previous example, maybe you want to retrieve the value for the "name" of the item. In this case, all you have to do is save the output as a variable, change it to a dictionary, and then request the item you desire. For example, first save the output as a variable, in this case, we'll call that "item": item = items_api.get_item_and_all_properties(id='95644F20-BD68-4DFC-9C15-E4E1D262369C') Then convert the output "item" into a dictionary and request whatever key you would like: item.to_dict()['name']
    3 points
  30. Background: Sensors or calculated tags that totalize a value over time often need to be reset due to maxing out the range of the sensor or the number of available digits in the calculation database. This can create a saw-tooth signal that resets every time this range maximum is reached. In actuality, the signal is constantly increasing rather than building up to the range max and then stepping down to zero to begin counting back up towards the max. Solution: Use Seeq Formula to convert the saw-tooth signal into a continuously increasing signal bounded in time by some reset period determined by the Subject Matter Expert. 1. In this example we begin with a saw-tooth counter signal that resets every time the sensor reaches its range max of 100. 2. Use Seeq Formula to convert the sawtooth signal into a continuous, increasing signal. Note that in order for Seeq to do this calculation, a bounding condition is required. This can either be a repeating periodic condition, or a condition created using the custom condition tool. This can be done in one step using the following code: //creates a bounding condition for running sum calculation $reset = years() //calculates running delta of signal between each sample, compares to zero to ignore negative running delta values, //calculates the running sum of the running delta signal over the bounding condition $signal .runningDelta() .max(0) .runningAggregate(sum(),$reset)
    3 points
  31. Seeq Data Lab allows users to programatically interact with data connected to Seeq through Python. With this, users can create numerous advanced visualizations. Some examples of these are Sankey diagrams, Waterfall plots, radar plots and 3D contour plots. These plots can then be pushed back into Seeq Organizer for other users to consume the visualizations. A common workflow that can stem from this process is the need to update the Python visualizations in an existing Organizer Topic with new ones as newer data become available. Here we'll look over the steps of how you can update an existing Organizer Topic with a new graphic. Step 1: Retrieve the Workbook HTML Behind every organizer topic is the HTML that controls what the reports display. We'll need to modify this HTML to add a new image will also retaining whatever pieces of Seeq content were already on the report. pulled_workbooks = spy.workbooks.pull(spy.workbooks.search({'Name':'Organizer Topic Name'})) org_topic = pulled_workbooks[0] # Note you may need to confirm that the first item in pulled_workbooks is the topic of interest ws_to_update = org_topic.worksheets[0] # Choose the index based on the worksheet intended to be updated ws_to_update.html # View the HTML behind the worksheet Step 2: Create the HTML for The Image The "add_image" function can be used to generate the HTML that will be inserted into the Organizer Topic HTML replace_html = ws_to_update.document.add_image(filename = "Image_To_Insert.png") replace_html Step 3: Replace HTML and Push Back to Seeq To find where in the Organizer Topic HTML to replace, we can use the re module. This will allow us to parse the HTML string to find our previously inserted image, which should begin with "<img src=". Note additional changes are required if multiple images are included in the report. import re before_html = re.findall("(.*)<img src=", ws_to_update.html)[0] # Capture everything before the image after_html = re.findall(".*<img src=.*?>(.*)", ws_to_update.html)[0] # Captures everything after the image full_html = before_html + replace_html + after_html # Combine the before and after with the html generated for the new picture ws_to_update.html = full_html # Reassign the html to the worksheet and push it back to Seeq spy.workbooks.push(pulled_workbooks)
    3 points
  32. I believe the unbounded error is because the original code creates conditions for >=0 and <0 with no maximum duration. You can change $gte and $lte to have max durations and this may fix the issue. $gte = ($delta >= 0).setMaximumDuration(7d) $lt = ($delta < 0).setMaximumDuration(7d) In your example, the reversal count will not count 3, it will count one. Let's transform your example as runningDelta() would and assume the previous value was also 5. 5 -> 5 -> 10 -> 15 -> 10 becomes 0 -> 5 -> 5 -> -5 Now let's identify the gte and lt conditions. We would see an $gte {0 -> 5 -> 5->}, and an $lt at {-> -5}. Use our formula and the result would be 1 reversal. However this does bring up a potential issue. If the example had another 10 at the end, the delta samples would become 0 -> 5 -> 5 -> -5 -> 0 and that would create an additional $gte capsule at the very end, resulting in 2 reversals. You could fix this by change the $gte to only greater than instead of greater than or equal zero. Thanks, Andrew
    3 points
  33. Sometimes when looking at an xy plot, it can be helpful to use lines to designate regions of the chart that you'd like users to focus on. In this example, we want to draw a rectangle on the xy plot showing the ideal region of operation, like below. We can do this utilizing Seeq's ability to display formulas overlaid against an xy plot. 1. For this first step, we will create a ~horizontal line on the scatter plot at y=65. This can be achieved using a y=mx+b formula with a very small slope, and a y-intercept of 65. The equation for this "horizontal" line on the xy plot is: 0.00001*$x+65 2. If we want to restrict the line to only the segment making the bottom of our ideal operation box, we can leverage the within function in formula to clip the line at values we specify. Here we add to the original formula to only include values of the line between x=55 and 5=60. (0.00001*$x+65) .within($x>55 and $x<60) 3. Now let's make the left side of the box. A similar concept can be applied to create a vertical line, only a very large positive or negative slope can be used. For our "vertical" line at x=55, we can use the following formula. Note some adjustment of the y-axis scale may be required after this step. (-10000*($x-55)) 4. To clip a line into a line segment by restricting the y values, you can use the max and min functions in Formula, combined with the within function. The following formula is used to achieve the left side boundary on our box: (-10000*($x-55)) .max(65) .min(85) .within($x<55.01 and $x>54.99) The same techniques from steps 1-4 could be used to create the temperature and wet bulb max boundaries. Formula for max temp boundary: (0.00001*$x+85).within($x>55 and $x<60) Formula for max wet bulb boundary: (-10000*($x-60)) .max(65) .min(85) .within($x<60.01 and $x>59.99)
    3 points
  34. Hi Julian, The first formula ($signal.toCondition()) will create a capsule the length of each time frame between step changes. From there, you can use Signal from Condition to calculate the "Total Duration" statistic for each of the capsules created (use the result of the formula for both the condition of interest and bounding condition).
    3 points
  35. Hi Vegini, I guess there are two ways to accomplish this. First one is using Seeq CLI using the command seeq datasource items on the Seeq server. As you can see below you are able to filter for specific datasources you are interested in and also export the results to a .csv file. To get the name, class or id of a datasource you can execute seeq datasource list first to get the desired information to be used for filtering: More information on the CLI can be found here: https://support.seeq.com/space/KB/215384254/Seeq%20Server%20Command%20Line%20Interface%20(CLI) The second way would be utilizing the API, for example in a python script. As you see you have to specify the ID of the datasource to get the associated items. The ID of a datasource can be determined by calling the datasources endpoint: Hope this helps. Regards, Thorsten
    3 points
  36. FAQ: I have a condition for events of variable duration. I would like to create a new condition that comprises the first third of the time (or 4th, or 10th) of the original condition. Solution: A stepwise approach can be taken to achieve this functionality. 1. Begin with your condition loaded in the display pane. 2. Create a new Signal using Signal from Condition that calculates the total duration of each of your event capsules, interpolated as a step signal. 3. Create a new signal that is your total event duration multiplied by the proportion of the event that you would like to capture. e.g. for the first 1/3 of the event, divide your total duration signal by 3, as shown below. 4. Create an arbitrary discrete signal with a sample at the start of each of your event capsules. 5. Shift the arbitrary discrete signal in time by the value of your signal calculated in step 3. In this example, the 1/3 duration signal. Note, depending on your version of Seeq, the function to do this may be called move() or delay(). 6. Use the toCapsules() function in Formula to create a tiny (zero duration) capsule at each of your shifted, discrete samples. 7. Join the start of your original condition with the capsules created in step 6 using the composite condition tool.
    3 points
  37. Sometimes it is desired to have custom units of measure display in Seeq Scorecards. This could be used when the signal or condition has no units or when you want to add a custom display or a unit that might not be a recognized Seeq unit. You can use Seeq's Number Format customization in the item properties panel to add custom text units to your scorecard. Here are some examples showing different ways to add text display units. The key here is including the text in quotation marks. More information on how to customize these number displays, including the syntax for adding custom text, can be found by clicking the "?" icon next to "Number Format".
    3 points
  38. FAQ: I have a signal with a gap in the data from a system outage. I want to replace the gap with a constant value, ideally the average of the time period immediately before the data. Solution: 1. Once you've identified your data gaps, extend the capsules backwards by the amount over which time you want to take the average. In this example, we want to fill in the gap with the average of the 10 minutes before the signal dropped, so we will extend the start of the data gap capsule 10 minutes in the past. This is done using the move function in Formula: $conditionForDataGaps.move(-10min,0min) 2. Use Signal from Condition to calculate the average of the gappy signal during the condition created in step 1. Make sure to select "Duration" for the timestamp of the statistic. 3. Stitch the two signals together using the splice function. The validvalues() function at the end ensures a continuous output signal. $gappysignal.splice($replacementsignal,$gaps).validvalues()
    3 points
  39. Frequently Asked Question: Is there a way to change the color of my signals overlaid in capsule time view to highlight the different capsules? Solution: One approach to changing the color of the signal being overlaid in capsule time view is to create separate signals for each desired display color that only contain samples during specific capsule(s). The examples below provide a step-wise approach to coloring based on either logic or time. Logic Based Coloring: Scenario: We start out with a temperature signal that we are overlaying based on a daily condition. We want the temperature signal to show up in red if the daily average temperature is greater than 80F, and blue if the daily average temperature is below 80F. 1. Switch back to calendar view and calculate the Average Daily Temperature using the Signal from Condition tool. In this example, we choose the "duration" time stamp to simplify subsequent steps. 2. Use the Value Search tool to identify days in which the Average Daily Temp signal is greater than 80F. 3. Repeat step 2 to find the days where the average daily temperature was below 80F. 4. Use Formula to break the original temperature signal into two pieces, one when daily average temperature is above 80F, and one when then daily average temperature is below 80F. The formula code to complete step 4 is: //take the temperature signal and keep only samples within the Avg Daily Temp < 80F condition $temp.within($DailyAvgLow) 5. Finally, switch back to capsule time view, and use dimming to display only your new "Temp signal during High Avg Daily Temp" and "Temp signal during Low Avg Daily Temp" signals. Use the "One Lane" and "One Y-axis" buttons to display in a single lane on the same axis. Time Based Coloring: Scenario: We have a temperature signal and we want to overlay data from the last 4 weeks in different colors so we can easily see changes in the signal from week to week. 1. Use Formula to create a condition for the past 7 days. //Create a condition with max capsule duration 8d, comprised of a single capsule that begins at now-7d and ends at the current time. condition(8d,capsule(now()-7d,now())) 2. Use Capsule Adjustments in Formula (move() function) to create a capsule for 2 weeks prior. //Shift the capsule for last week back in time by 7d. $lastWeekCapsule.move(-7d) 3. Repeat step 2, shifting by -14d and -21d to create capsules for 3 weeks prior and 4 weeks prior. 4. Use Formula to break the original temperature signal into 4 pieces, one for each of the previous 4 weeks. //Create a new signal from the original temp signal that contains only samples that fall within the prior 7d. $temp.within($lastWeek) 5. Switch to capsule time view, and put all the new temperature signals on one lane and 1 y-axis.
    3 points
  40. Hello Arnaud, the error is occuring because of the unit of the signal. I guess the unit of your signal is "t" which is converted to "t²" and "t³" for the respective parts of the formula. Therefore they cannot be used in an addition or subtraction. To get the fomula working simply convert the signal to a unitless one before calculating the polynom. The resulting signal is unitless. You can use the setUnits() function to specify a unit if you need one. Hope this helps. Regards, Thorsten
    3 points
  41. This week, I used the Journal link approach suggested by Kjell for 40 similar assets ! (Yes 40) and created couple of scorecard tables and output them to an organizer topic. This exercise was a good test of hand eye coordination and patience 🙂 Hopefully in a near future, I can rely on better and more efficient experience in seeq for this use case.
    3 points
  42. Hi Dominic, You can use the splice function in Formula to complete this request. I'm assuming your cost changes each year so you may want to splice in a different cost for each year. For example, you can use the following Formula to say take the current cost of $20 and splice in $10 cost for all of 2019 so that on January 1st, it steps up to the new cost: 20$.tosignal().splice(10$.tosignal(), condition(1y, capsule('2019'))) Let me also break this Formula down a bit so that you understand it better. In the first part, we are saying take a value of $20 (as a signal) as the default result, which means that anytime before or after 2019 (in this case), the value would equal $20. However, when the capsule is present in the condition in the splice condition is met (in this case it's a capsule for all of 2019), the $10 signal will be spliced instead of the $20 signal. I also want to note that the '1y' argument in the condition is the maximum duration. If you wanted to expand the capsule to be longer than 1 year, you would also need to edit that value. If you want to add additional years at the $10 value, you can simply add them as more capsules in the condition argument. For example, adding 2018: 20$.tosignal().splice(10$.tosignal(), condition(1y, capsule('2019'), capsule('2018'))) If you want to add different values instead (let's say $15 for 2018), you could use the following modification: 20$.tosignal().splice(10$.tosignal(), condition(1y, capsule('2019'))).splice(15$.tosignal(), condition(1y, capsule('2018')))
    3 points
  43. Hi Tommy, The easiest way to do this in Seeq is to use a condition to define the if condition, and then splice in a new signal when your condition is true. Follow the steps below to achieve this. 1) Use the Value Search tool to find when your signal .OP <= 0 2) In Formula, enter the following: $flowsignal.splice(0.toSignal(), $conditionclosed) where $flowsignal is your Flow Rate signal, and $conditionclosed is the condition we created in Step 1. What we are doing here is splicing in a new signal we create ( 0.toSignal() ) which will equal 0 when the .OP <= condition is true. You could also write all of this into 1 Formula (combining steps 1 & 2 together) by writing the following: $conditionclosed = $OP.validValues().valueSearch(isLessThanOrEqualTo(0)) $flowsignal.splice(0.toSignal(),$conditionclosed) Please let me know if this solved your question. -Kjell
    3 points
  44. This has been fixed in version R21.0.44.00
    3 points
  45. During a complex analysis, your Workbench can become cluttered with intermediate Signals and Conditions. Users utilize the Journal tab to keep their calculations documented and organized, often in the form of a Data Tray. If you have been adding Item links one-by-one, try using this trick to add all (or a large subset) of your items to your Journal all at once: Select all items in your display Click the Annotate button on the Toolbar Cut the Item links from your Annotation Paste the links into your Journal
    3 points
  46. In this example we want to plot both temperature signal and a model signal vs relative humidity in one scatter plot using Seeq. Here are the Steps: RAW Data: Temperature (Area A) Temperature (Area B) X axis - Rel humidity (Area A) Step 1. Delay one of the signals to make sure the data points for both signals are not aligned Model (Area B) $TempAreaB.delay(1min) Step2. Combine the model and the temperature into one signal and set the max interpolation to 1 sec so it doesn't draw a line in between samples Model & Temp Combined (Area A) $Temp.combinewith($Model).setmaxinterpolation(1sec) Step3. Create a condition per each sample in the Temperature and Model Signal Temp Condition (Area A) $Temp.setmaxinterpolation(1sec).isvalid() Model Condition (Area B) $Model.setmaxinterpolation(1sec).isvalid() Step 4. Go to Scatter plot and select "Relative Humidity" as the X-Axis and "Model &Temp Combined" as the Y Axis Step 5. In Scatter Plot, select Model condition and Temperature condition for condition based coloring the sample points
    3 points
  47. Hi Chris- I think this can be achieved using a combination of the Value Search and Composite Condition tools. In the following screenshot, I have a signal (temperature) and a condition (Start Capsule). Let's say I'd like to create a new condition that starts at the start of each capsule in Start Capsule and ends when the temperature is 90. First, use the Value Search tool to identify when the temperature is 90 F. This results in several small capsules each time the temperature is 90 F. Next, combine these 2 conditions using the join operator in the Composite Condition tool: This results in a new condition (Start to Temp=90) where each capsule starts at the start the Start Capsule capsule and ends at the start of the Temp=90 capsule. Please let me know if you have any additional questions. Thanks, Lindsey
    3 points
  48. FAQ: When in Scatter Plot view, is it possible to see the time stamp of a given sample? Specifically, I would like to know when outliers are occurring so that I can navigate back to trend view and further investigate what else was going on during that time frame. How to visualize the time frame: The best way to find a time frame of an outlying data point with the current version of Seeq is to make a condition that encapsulates that data point. The following example shows how to identify what your condition should be, create a condition, color code your scatter plot to highlight samples that occur during your capsules, and view the start and end time of your capsules in the capsules pane. 1. We have a scatter plot comparing two variables with some outliers, and we would like to know what the time stamps are when those outliers are occurring. In the example shown below, we have outlying data points when the Coil Inlet Temperature is below about 460F, and when the Flue Gas Exit Temperature is below about 805F. 2. We will create a condition for each of the outlying data point scenarios described above. In this example, we will do so using a simple value search for each. 3. Now when we return to scatter plot view, we have the ability to color our scatter plot based on various capsules. In versions prior to R.21.0.44, this is done using the "Capsules" button at the top of the Scatter Plot Display. In R.21.0.44 and later versions, this is done with the "Color" button. You can select Color based on conditions, and add your conditions. Then your scatter plot will show samples that fall within the capsules of your conditions in the color specified for your condition in the Details pane. 4. Add the capsule end time to the capsules pane to get the full time range that you want to further investigate in trend view. You can also utilize Seeq's Chain View feature from trend view to view what was going on with your signals of interest during your conditions.
    3 points
  49. Hi Jitesh, I tried to develop a nested if with the following logic: if (temp > 66) { if (rh > 50) { temp = 1/temp ; } else if (wetbulb < 75) { temp *= 2; } } If no condition is met, the temperature should not be modified. However it would be great if you could provide an example of what you are trying to achieve. Regards, Thorsten
    3 points
  50. Summary There are many use cases where the user wants to do an aggregation over a periodic time frame, but only include certain values. Examples abound: for cement calculate the average daily clinker production only when the kiln is running, for biotech pharma the standard deviation of dissolved oxygen only when the batch is running, etc. Here our user is looking into equipment reliability for compressors. She wants to calculate the average daily compressor power to examine its performance over time. Steps 1. Add the signal into Seeq. 2. Use the 'Periodic Condition' or 'Formula' Tool to add a condition for days. This doesn't have to be days, it can be any arbitrary time, but it is usually periodic in nature. To use a custom periodic Condition, consider using the periods() function. For example to do this for days, the formula is: periods(1day) As an example, to change this to 5-minute time periods the formula is: periods(5min) Here are how the days and statistics in the Capsule Pane look for her: 3. Find only the desired values to be used in the aggregate (e.g. Average) statistic. From the values in the Capsule Pane she sees that the average compressor powers results are too low. This is because all the time when the compressor was OFF, near 0, are included in this calculation. She wants to only include times when the compressor is running because that will provide a true picture of how much energy is going into the equipment - and can indicate potential problems. To do this, she creates a 'Value Search' for times to include in this calculation, in this case when the Compressor is ON or > 0.5kW. 4. Create a new signal to only have values *within* the 'Compressor ON' condition. Use the aptly named 'within' function in the 'Formula' Tool. Notice how the resulting Signal in the bottom lane only has values within the 'Compressor ON' condition. Now she can use this because all those 0's that were causing the time-weighted Average to be low... are now gone. 5. Examine the resulting statistical values. She now sees that the calculations are correct and can use this resulting 'Compressor Power only when ON' Signal in other calculations using 'Signal from Condition', 'Scorecard Metric', and other Seeq Tools!
    3 points
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