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Found 19 results

  1. How do I make a capsule from a condition? I am very new to Seeq, and it is very much possible that I am doing this in a more complicated way than necessary, so I will try to explain the goal and problem I have. I have a signal which I have filtered to only show me the interesting regions. In these regions, the signal has a start value of about 38 and slowly decreases until around 34 around which there is a small bump and then the signal decreases again until it reaches a another bump between 25 and 23. There is some variations for the different regions. My goal is to find the time between the lowest point of the first bump and the first occurrence after where the signal has a value of 25.2. Depending on the second bump, there might be several such points after the first bump, or only one. What I have done: I took the derivative of the signal and smoothed it using the agileFilter to be able pick out the first bump (This also gave me the second bump, but I don't care about that). Then I did a valueSearch to get capsules for when the smooth derivative is greater than 0.001 for at least 1 minute, which gave me a capsule for each peak. Then I only focused on the first bump/peak by using intersect to get capsules for when the filtered signal (blue) is greater than 30 and overlapping with the smooth derivative peaks (capsules). Then I tried to get the minimum value of the signal that lies within the duration of the newest capsule, using the minValue function. However, this did not work, because the newest capsule is actually a condition. Does anyone know how can I make the condition into a capsule, or get around this issue? Any help would be greatly appreciated 🙂 PS. I am trying to do this using the Formula tool.
  2. Asset groups and asset trees (link) are used frequently in Seeq for asset swapping calculations, building treemap visualizations, and scaling tabular results across assets. In some cases, users want to export calculated results for assets from Seeq Workbench to Excel or perhaps to Power BI. The following example illustrates a technique for doing this efficiently with a single export. 1. Asset group for 8 furnaces contains an "Outlet Temperature" signal that we want to calculate and export daily statistics (avg, std deviation, min, max) for: 2. Note that the "Daily Statistics" condition is created with a Formula that is part of the asset group. This is key to enabling the data export across all assets. See the formula for the "Daily Statistics" condition below for Furnace 1. Note that we create a daily condition and then add the temperature statistics as capsule properties, and assign an asset name property. These details are also essential in setting up an efficient export. As a reminder, we need to edit the "Daily Statistics" formula for each furnace to assign the correct furnace number to the Asset Name capsule property. For this example (only 8 assets), this is easy to do manually. For a large asset group (50, 100 or more), a better approach would be to create an asset tree using Data Lab, and programmatically create the individualized "Daily Statistics" conditions. 3. Next, we navigate the asset group and add the Daily Statistics condition for each furnace to the trend display in Workbench, which makes it easy to set up the "Daily Furnace Statistics for Export" in step 4. 4. Create the "Daily Furnace Statistics for Export" condition which will have overlapping daily capsules for the 8 furnaces. Here, we combine the separate Daily Statistics conditions (for all 8 furnaces) into a single condition. For the export to work as expected in a later step, we need to slightly offset the capsules using the move() function, so that they do not have identical start times. 5. Next, we visually check the capsules and their properties on the trend (Asset Name and Daily Avg) and in the capsules pane on the lower right. Everything looks as expected, we have a capsule for each furnace for the day of May 24. 6. The export to Excel or to other applications via OData can now be set up. The key is to export only the "Daily Furnace Statistics for Export" condition, and to set the time range appropriately based on your objectives. Here, we only want the results for 1 day: 7. Checking the export on the Excel side, all looks good. We have the daily statistics, scaled across all furnace assets, with one row for each furnace: To summarize, the following are keys to this technique for exporting calculation results across all assets, from Seeq Workbench: Store the calculation results as capsule properties in a condition that is an item in the asset group, and also assign an asset name property (see Step 2 above). In this example we used a daily time basis for calculations, but the techniques can be applied and extended for many scenarios. To store the results across all assets, create a single condition for export, which is a combination of all the individual asset "calculation results" conditions, and offset capsules slightly as needed to avoid capsules having identical start times (see Steps 3 and 4 above). In this example, we only had 8 assets so all formulas could be created interactively by the user. For large asset structures, the asset tree and formulas, including the final condition for export, can be created programmatically using Data Lab (link).
  3. Part One: Creating an Asset Group & Treemap In this video you will learn when to use asset groups in Seeq, how to create an asset group & add calculations, and how to build a Treemap to monitor many assets at once. Part Two: Creating Tables & Charts In this video you will learn how to view log or alarm conditions in Seeq, create a condition table, a simple table, and a chart. Part Three: Creating an Organizer Topic Dashboard In this video you will learn how to create an asset performance monitoring dashboard in Seeq Organizer Topic.
  4. The correlationOffset() Formula function can be a useful tool for identifying the time shift which maximizes the correlation between two signals: see this post for additional background. In some situations, a user will have a condition defined for time periods of interest (startups, process runs, specific product grades, specific modes of operation, etc.). The user then wants to analyze how the correlation offset varies for each time period of interest (each capsule in the condition). The key to this calculation is applying the transform() function to the condition in Seeq Formula, in combination with the correlationOffset() function. Let's say we have a temperature sensor in a reactor. At some point, well downstream of this temperature measurement, we have a relative humidity sensor that is sensing the same volume of air, but due to the locations of the two sensors, we know that the inverse correlation between the two signals is offset by a significant amount of time delay (at least 2 hours, as visually estimated with the dashed regions in the trend below). As a reminder, for this use case, the objective is to calculate the correlation offset separately for the data contained within each capsule in a condition of interest (shown as Time Periods to Calculate Correlation in the trend below): The formula approach for this is shown below, with comments to describe the details. The transform() function enables the correlationOffset() function to be applied separately to each capsule in the Time Periods to Calculate Correlation condition, and the correlation offset time is stored as a capsule property of the condition generated by the formula: The resulting calculated offset (in units of seconds) is shown in the capsules pane at the lower right and also at the top of the screen as labels. Optionally, the "Offset" capsule property can be converted to a signal (see Max Correlation Offset in lane 2) for trending purposes, and here the units were converted to hours. Looking at the final results, the time shift which maximizes the correlation between the 2 signals varies between 2.1 and 2.5 hours over the 3 time periods of interest shown in chain view, and this variation may offer valuable insights to the user. The time shift is a negative value which means that the relative humidity (downstream signal) would need to be shifted to the left by that time amount to maximize its correlation with the temperature signal. This is the formula to create a signal for the max correlation offset, based on the "Offset" capsule property. In this example the time shift is more meaningful in units of hours, so we convert from seconds to hours: Note that in this use case we wanted to calculate the correlation offset separately for each capsule in a condition. If the goal is to calculate the correlation offset over rolling window time periods, there are other functions in Formula expressly for this purpose, such as CrossCorrelations_timeShifts().
  5. Question: I have a condition with multiple properties that I have displayed in the capsules pane. Is there a way to get the data from the capsules pane into a Seeq Organizer Topic?
  6. I have a report i need to generate that has multiple potentially overlapping time periods. How can i get these times into my table? In my case, i want to calculate some statistics in my table over multiple variable time periods such as "April 2019", "Quarter 1", and "Year to Date", etc. 1
  7. I have a batch operation characterized by two conditions: The first condition represents the start of the batch and the second condition contains the end of the batch. Each capsule has the batch ID as a capsule property. How can I make a single condition that represents the duration of the batch by joining the start and end conditions?
  8. In Seeq's May 25 Webinar, Product Run and Grade Transition Analytics, we received a lot of requests during the Q&A asking to share more details about how the grade transition capsules were created and how they were used to build the histograms presented. So here is your step-by-step guide to building the condition that will make everything else possible! Defining your transition condition Step 1: Identifying product campaigns The "condition with properties" tool under "identify" can transform a string signal of product or grade code into a single capsule per campaign. In older versions of Seeq this same outcome can be achieved in formula using $gradeCodeStringSignal.toCondition('grade') Note: if you don't have a signal representing grade code, you're not out of luck, you can make one! Follow the general methodology here to search for set point ranges associated with different grades or combinations of set points that indicate a particular grade is being produced. Step 2: Create the transition condition encoded with the starting and ending product as a capsule property $t = $c.shrink(2min).inverse() //2 min is an arbitrary small amound of time to enable the inverse function to //capture gaps in the campaigns $t.removelongerthan(5min) // 5min is an arbitrarily small amount of time to bound calc (that is >2min * 2) .transform($capsule -> $capsule.setProperty('Transition Type', $pt.resample(1min).toscalars($capsule).first() + '-->' + $pt.resample(1min).toscalars($capsule).last())) .shrink(2min) Step 3: Identify time period before the transition Step 4: Identify time period after the transition Step 5: Optional - for display purposes only - Show the potential transition timeline (helpful for chain view visualizations) Step 6: Create a condition for on-spec data Here we use value search against spec limit signals. If you don't have spec limit signals already in Seeq, you can use the splice method discussed in the post linked in step 1 to define them in Seeq. Step 7: Combine condition for on-spec data and condition for time period before the transition Step 8: Combine condition for on-spec data and condition for time period after the transition Step 9: Join the end of the condition for on-spec before the transition to the start of the condition for on-spec after the transition to identify the full transition duration Your condition for product transitions is now officially ready for use in downstream applications/calculations! Working with your transition condition Option 1: Calculate and display the transition duration Use Signal from Condition to calculate the duration. The duration timestamp will generate a visual where the length of the line is proportional to the length of the transition. Increasing the line width in the customize panel will display the transition duration KPI in a gantt-like view. Option 2: Build some histograms Summarize a count of capsules like the number of times each distinct transition type occurred within the date range. Summarize a signal during each unique transition type, like the max/min/average transition duration for a distinct transition type. Option 3: Generate some summary tables Switch to the table view and toggle "condition" to view each transition as a row in the table with key metrics like total duration and viscosity delta between the two grades. Add the "transition type" property from the "columns" selector. If you run into hiccups using this methodology on your data and would like assistance from a Seeq or partner analytics engineer please take advantage of our support portal and daily office hours.
  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. Datasources can also bring in conditions with capsule properties already added. This guide will walk through some common workflows to visualize, add, and work with capsule properties. 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. In the capsule pane, you can filter on capsule properties by clicking on the three dots next to a column. In Trend View, you can add Capsule Property labels inside the capsules by selecting the property from the Labels drop down at the top of the trend. 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 Condition Table, you can add capsule properties as columns or as headers How do I create a capsule for every (unique) value of a signal? The Condition with Properties tool allows you take one or more step signals and turn them into a condition with a capsule per value change. The signal values will be added as properties on the capsules. For instance, if we have a BatchID and an Operation signal, we can use this tool to generate a capsule per Operation. In formula this would be done using the toCondition() operator: $batchID.toCondition('Batch ID') Where Batch ID is the name of the resulting property on the condition. 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 can also use the setProperty operator. 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. $condition.setProperty('Property Name', $signal, aggregationMethod()) A full list of aggregation methods can be found under the Signal Value Statistics and Condition Value Statistics pages in the formula documentation, but common aggregation methods include: startValue() endValue() max() average() count() totalDuration() Option 3: Moving properties between conditions (e.g. parent to child conditions) 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. You can accomplish this using mergeProperties() $conditionWithoutProperty.mergeProperties($conditionWithProperty) 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. 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), the end timestamp of the capsule (endKey), or the entire duration of the capsule (durationKey). For most use cases the default of durationKey is the most appropriate. $condition.toSignal('Property Name') // will default to durationKey $condition.toSignal('Property Name', startKey()) $condition.toSignal('Property Name', endKey()) Using startKey or endKey will result in a discrete signal. 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 step interpolation of capsules may look like the following formula: $condition.toSignal('Batch ID', startKey()).toStep(40h) 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: $upstreamCondition.move(0, 7d).mergeProperties($downstreamCondition, 'Batch ID').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 merge properties with the downstream condition, only bringing properties from overlapping downstream capsules with the same Batch ID as the upstream condition. Using capsule properties in histogram You can create bins using capsule properties in the histogram tool by selecting the 'Condition' aggregation type. The following example creates a Histogram based upon the Value property in the toCondition() condition. The output is a Histogram with a count of the number of capsules with a Value equal to each of the four stages of operation: Creating Capsule Properties Reference Video: Conditions and capsules in Seeq are key to focusing #timeseries data analytics on specific time periods of interest. In this video, we explore how to create capsule properties to add additional context to the data. Using Capsule Properties Reference Video: Conditions and capsules in Seeq are key to focusing analytics on specific time periods of interest. In this video, we explore how to use capsule properties, the data attached to an event, to supply further insights into the data. Content Verified DEC2023
  10. Use Case: Users are often interested in identifying when a particular process is operating in a specific mode, or when it is in transition between modes. When looking at these transition periods, you may want to know what the modes of operation were immediately before and after the transition. If you can assign the starting and ending modes during a transition period to each transition capsule, you can filter for specific types of transitions and get a better idea of what to expect during like transitions. Solution: For Versions R.21.0.43 + 1. Add a signal to your display that describes the mode of operation you are interested in. In this example I have added the Example Data > Cooling Tower 1 > Area A > Compressor Stage string signal. Other signals that this use case applies to may include: production grade code, equipment operating mode, signal for step in a sequential or batch process. 2. Next we can use Formula to create a new condition comprised of capsules each time our Compressor Stage signal changes value. These capsules will contain a new capsule property called 'StartModeEndMode' that represents the value of the compressor stage immediately before and immediately after the signal changed value, or transitioned. The formula syntax to achieve this is: //creates a condition for 1 minute of time encompassing 30 seconds on either side of a transition $Transition = $CompressorStage.toCondition().beforeStart(0.5min).afterStart(1min) //Assigns the mode on both sides of the step change to a concatenated string that is a property of the capsule. $Transition .transform( $cap -> $cap.setProperty('StartModeEndMode', $CompressorStage.toCondition() .toGroup($cap, CAPSULEBOUNDARY.INTERSECT) .reduce("", ($seq, $stepCap) -> $seq + $stepCap.getProperty('Value') //Changes the format of the stage names for more clear de-lineation as a property in the capsules pane. .replace('STAGE 1','-STAGE1-').replace('STAGE 2','-STAGE2-').replace('TRANSITION','-TRANSITION-').replace('OFF','-OFF-') ))) and the Output is: Note that we added the new property 'StartModeEndMode' to the Capsules Pane. 3. We can now filter this condition to look for specific transitions of interest. In this example, we are interested in every time our compressor went from a TRANSITION state to STAGE2. Use Formula and the filter() function with the following syntax to achieve this. //Create a new condition comprised only of capsules where the 'StartModeEndMode' property is equal to '-TRANSITION--STAGE2-' $ConditionCreatedInStep2.filter( $capsule -> $capsule.getProperty('StartModeEndMode').isEqualTo('-TRANSITION--STAGE2-')) and the Output is: 4. Now we are able to add other signals of interest to the display and switch to Capsule Time view to observe how those signals behave during these similar transition events.
  11. a. Add the property of the day of the year to the daily condition. 1. From Tools>>Identify select Periodic Condition >> Daily and click execute. The daily condition automatically includes the properties of the Day of the week, month and the day of the year. 2. From the Capsules pane select the required properties. b. Add the property if the week of the year to the weekly condition. 1. Use the following Formula to create a weekly condition with the property of the week of the year. round(timesince(years(),1week).tostep() + 1).toCondition('Week') 2. From the Capsules pane select the Week property.
  12. FAQ: I have a CSV file that has the start and end times of some historical events and various information about the events that I would like to use in my analysis in Seeq. How do I go about getting these events and all of their associated information into Seeq? Solution: Use the Import from CSV tool and Seeq Formula to bring in a condition comprised of each of these events and assign the data in each column of the CSV as a property of the condition. 1. Ensure your CSV file is formatted correctly for import into Seeq. The first column should be the event start time, the second column should be the event end time, and all other data columns should be to the right of these. A list of acceptable timestamp formats can be found on the Seeq Knowledge Base in this article. 2. Use the Import CSV File tool to bring the condition into Seeq. Drag and drop your CSV file or navigate to your file through Windows (Mac) Explorer. Under "Import File as" select "condition". Choose the start-time and end-time columns in the "Choose columns" section. Specify a max capsule duration that is just longer than your longest event. 3. Once your condition is imported, use Seeq Formula to assign the data from the other columns of our CSV as properties of each capsule. Begin by using the item properties for the CSV imported condition to duplicate the condition to Formula. Once in Formula, add the column headers from your CSV to the query in line 1 of the code, separated by commas. Then use the setProperty() function to assign each of the columns of the CSV as a property of each capsule. Once executed, the output is a new condition that looks exactly like the original from trend view in the display pane. However, this new condition has properties, that can be added to the capsules pane using the gridded+ button.
  13. 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.
  14. FAQ: We have various conditions that are calculated from signals on a variety of different equipment and assets. We would like to view them in a histogram that is broken out by month, and for each month each asset has a separate bar in the histogram. Example Solution: 1. For three signals, we want to create a histogram that is the total time per month spent above some threshold. In this example, each signal is associated with a different cooling tower Area. 2. We have a condition for when each signal is above it's threshold value. These conditions were created using the value search tool. 3. The three conditions can be combined into a single condition (here it is called "Combined In High Mode w Area as Property"). In the formula tool, before combining the conditions, we assign each condition a property called 'Area' and set the value as that particular asset area. Once the properties are set we use the combineWith() function to combine them into one final signal. The formula syntax below will achieve this: //Create a new condition for each original condition that has a property of 'Area'. $A=$AHigh.setProperty('Area','Area A') $G=$GHigh.setProperty('Area','Area G') $I=$IHigh.setProperty('Area','Area I') //Combine the new conditions created into a new condition with all of the high power modes where each capsule //has a property of 'Area' that describes the signal that was searched to identify that original condition. combineWith($A,$G,$I) ***Note: the combineWith() function in Seeq Formula is required here because it will retain capsule properties of individual conditions when combining them. Using union() or any other composite condition type logic will NOT retain the capsule properties of the individual condition.*** 4. Use the Histogram tool and the multiple grouping functionalities to aggregate over both time, and the capsule property of 'Area'. Final Result: (remove other items from the details pane to view just the histogram)
  15. Goal is to create a string signal that shows the different operation modes in a device at each time. Step 1. Create the sample modes using Values Search Tool, Advanced Search: Value Search Tool, Advanced Search Step 2. Create a continuous condition called “Mode” comprising all three (startup, steady state, shutdown), and assign a property to each capsule to identify the mode. for more information please look at the following link: Set property to a capsule Formula for creating the mode with property condition: $ShutDown = $Shutdown.setProperty("Mode", "ShutDown") $StartUp = $Startup.setProperty("Mode", "StartUp") $steady = $Steady.setProperty("Mode", "Steady") combineWIth($StartUp, $steady, $ShutDown) Step 3. Use the Formula Tool to create a string signal using the "mode with property" condition and shows the mode of the input signal at each time. Here is the formula for creating the string signal (Mode is the Mode with property condition in here) : $inputsignal.transformToSamples( $capsule -> sample($capsule.getStart(), $capsule.getproperty('Mode')), 10d)
  16. Issues with different devices can lead to shutdowns and failures. In this example we want to monitor the duration of each operation mode of an input signal. This example can be used to monitor all the conditions that can lead to inefficient device performance. In order to monitor all operation modes we need to, 1. Create all the modes using Value Search Tool. a. StartUp (input < 75) b. ShutDown (input > 95) c. SteadyState (75 < input < 95) 2. Create a continuous condition called “Mode” comprising all three (startup, steady state, shutdown), and assign a property to each capsule to identify the mode. for more information please look at the following post: A Guide to Working with Capsule Properties $ShutDown = $Shutdown.setProperty("Mode", "ShutDown") $StartUp = $Start.setProperty("Mode", "StartUp") $steady = $Steady.setProperty("Mode", "Steady") combineWIth($StartUp, $steady, $ShutDown) 3. Finally, calculate the total time per mode using Histogram Tool. For more information on Histogram Tool please see the following link: Histogram Tool Content Verified DEC2023
  17. For a detailed guide to working with capsule properties, please see this tips and tricks guide: Content Verified DEC2023
  18. Hi Seeq- Is there a way to create a histogram with variable size bins?
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