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1. Summary: Many of our users monitor process variables on some periodic frequency and are interested in a quick visual way of noting when a process variable is outside some limits. Perhaps you have multiple tiers of limits indicating violations of operating envelopes or violations of operating limits, and are interested in creating a visualization like that shown below. Solution: Method 1: Boundaries Tool One method to do this involves using the boundaries tool. This tool is discussed in Step 3 of this seeq.org post, and results in a graphic like that shown below. Some frequently asked questions around the above method are: Is there a way to make the different levels of boundaries different colors? Is there a way to color the section outside of the limits rather than inside of the limits? Method 2: Scorecard Metrics in Trend View Step 1. Load the signal you are interested in monitoring as well as the limits into the display pane. The limits can be added directly from the historian, or if they do not exist in the historian they can be created using Seeq Formula. Step 2. Open a new Scorecard Metric from the tools panel, create a simple scorecard metric on your signal of interest, with no statistic. Click the "+" icon to optionally enter thresholds, and add the threshold color limits that you are interested in visualizing. Note that the thresholds input in the boundary tool can be constant (entering a numeric value) or variable, selecting a signal or scalar.
2. I have a condition with several capsules of various lengths; how do I determine the time period or length of each capsule? For example, my first capsule is 24 hours long, and then the next capsule is only about 3 hours. I'm looking for a way where I can get this information for all of my capsules.
3. We often would like to summarize data in a table to reflect something similar to below: There are a couple ways to achieve this in Seeq. In this example, we'll explore using Simple Table view to get this result. If you're interested instead in using Conditional Scorecard Metrics, I would take a look at this Seeq.org post! Step 1: Goto Table view & Select Simple Under Columns, ensure Average, Last Value, and Name are selected Step 2: Rearrange & rename the Headers; Last can be moved to 2nd column and renamed to Current. Avg (now 3rd column) can be renamed to 1 hr avg. Step 3: Copy the link and paste it into an organizer topic. Create a new Date Range named 1 hr (with a duration of 1 hr) to assign to your table. After clicking the table & Update Seeq Content: Step 4: Can be done on the same worksheet, or a new worksheet. I will create a new worksheet. Back to Simple table, remove the Name column so only Average is selected. Rename this column to 24 hr avg. Step 5: Paste this worksheet into your organizer next to your other table. Create another Date Range named 24 hr (with a duration of 24 hr) to assign to this newly added table (similar to Step 3). Step 6: Click each table to then click the Toggle Margin button. When complete, the table should look like one single table. To update the date range for the entire table, simply click the "Step to current time signal" next to Fixed Date Ranges.
4. This question came in through the support channel today and I thought it was a good opportunity to explain some of the nuances in the Scorecard tool and give a couple of potential work arounds. Question: In this example we are using two simple Value Searches on the Example -> Area A dataset Running -> Compressor Stage !~ "off" Stage 2 Running -> Compressor Stage ~ "stage 2" Unfortunately in Seeq there is not an easy way to directly perform Math or formula like operations on Scorecard Metrics created using the "Simple" option like you see here above. The "Simple" metrics are reactive to your display range and so have more limitations as compared to the "Condition" and "Continuous" options. In order to create this calculation we need to first decide on a calculation range that matches our engineering or business need. In this case we want to calculate this value per month. Option 1 - Convert to Signal from Condition Calculations Create a Monthly condition using the Periodic Condition tool Create Monthly Total duration in each mode calculations using the Signal from Condition Tool Use Formula to Create your % Time running calculation (\$Stage2Duration/\$RunningDuration).convertUnits("%") Create Scorecard using Condition Option Option 2 - Create Continuous Calculation Signal The main downside of Option 1 is that you cant see at any given point in time how the last running 30 days have been performing like you could with the original simple scorecard metrics. The following formula will build a calculation of the % Time in Stage 2 of total running time for the trailing 30 days calculated every hour \$aggPeriods = periods(30days,1h) \$RunningDuration = \$Running.removeLongerThan(1d).aggregate(totalduration(),\$aggPeriods,endkey()) \$stage2Duration = \$Stage2Running.removeLongerThan(1d).aggregate(totalduration(),\$aggPeriods,endkey()) (\$stage2Duration/\$RunningDuration).convertUnits("%") This calculation can be added in a simple scorecard metric as well and as long as the simple scorecard duration is 30 days it will give you the desired results.
5. Here’s an example on how to add color threshold so that it alerts when the current value to day deviates by +-5 degF Step 1: Calculate daily average temperature using signal from condition. Step 2: Create the high and low limits in the formula as below:- High Limit: (\$averagetemperature.move(1d))+5 Low Limit: (\$averagetemperature.move(1d))-5 Step 3: Add the high and low limits in the metric's threshold as below screenshot:-
6. 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.
7. Hey there, My question splits into two parts: Firstly, I want to create a condition based on multiple criteria: if signal A equal to 3, B equal to 4, C is greater than 5 than condition is valid. I know i could create 3 individual capsule and overlap them. Is there a simple way to use formula to do so? Secondly, in my analysis i have 10 signals and associated conditions(alert), then I want to know in the past 7 days how many alert in total(repeated instance or capsule doesnt count) ? and How long is the total alert time? Thank you
8. Background: One of the quirks of raw, ungridded time series data is that sampling frequencies may vary. Sometimes the sample frequency is different for two signals that you are comparing, and sometimes the sample frequency is different for a single signal at different points in time due to a process data historian's compression configuration. How you handle this variability in sampling rate can have a significant impact on calculations of summary statistics. Various approaches to calculating summary statistics and their implications: In this example we have four signals with different amounts of samples in the display range (as highlighted by the "count" statistic in the details pane. Our goal is to calculate a single "average" value for all of the signals during this window. Notice the different outcome values from each different approach. Method 1: One option that we have for getting a single "average" statistic is to take the average value of each of the 4 signals over the time window, then take the average of that. This method weights each of the signals evenly in the calculation of the final average value, since the final average value is equal to (0.25)*avg1 + (0.25)*avg2 + (0.25)*avg3 + (0.25)*avg4 Method 2: A second option is to first create a continuous average signal, then aggregate that over the display window to calculate an average. The average can be calculated using formula and the average function with the syntax shown below. Note that the sample count on the output signal has a significantly larger number of samples than any of the original signals. This is because the average function calculates a sample any time any of the input signals has a sample. For the signals that do not have a sample at a particular key, the linearly interpolated value of the signal is used in the average calculation. Then a scorecard aggregation of the average of the continuous average signal can be calculated in the Scorecard Metric tool to get the result below. Method 3: A third option is to take the average of all the data points in the display window, independent of which signal they belong to. This approach involves first combining all of the samples from the 4 signals into a single signal, then taking the average of that value over the display window. The sample count of the combined signal will be equal to the sum of the sample counts of all other signals, as demonstrated below. In this approach, the resultant signal also has a sample any time any of the other signals contain a sample. Note that if signals have the same frequency, a tiny delay can be applied to the 2nd through the nth signal (1 ns to n-1 ns) to ensure all samples are kept. A scorecard metric of the average of the combined signal can then be calculated. The overall average value returned using this method is much higher than the two previous methods due to the relatively higher amounts of samples in signals 3 & 4, which have generally have higher values than signals 1 & 2. Method 4: A non-time-weighted average (similar to method 3) can also be calculated using the following formula: average(\$signal1.toDiscrete(),\$signal2.toDiscrete(),\$signal3.toDiscrete(),\$signal4.toDiscrete()) Once again the final signal contains a number of samples equal to the sum of all of the sample counts of the input signals. In conclusion.. Which method of averaging is best for your use case? The answer is probably "it depends" on the use case you are analyzing. Some examples of when different methods may be applied: An average over a specific time range - Method 1 An instantaneous average at a point in time - Method 2 An average of signals where each sample represents a unique event or independent measurement (e.g. lab or quality data) - Method 4 Regardless of your specific use case, having an understanding of how your data frequency, your historian's compression settings and your analytical approach can impact your results is an important starting point in any analysis!
9. Question: I have a scorecard metric displaying the maximum value of a signal during a given capsule. I have the Scorecard coloring red if the value is >2%. Is there any way to display a scorecard with just color coded cells containing no values? What I am seeing now: What I would like to create: Solution: In general, the approach to creating a blank scorecard with color thresholds is to create a string signal comprised of varying amounts of spaces for each threshold/band. Then you can apply that number of spaces as a color threshold in the Scorecard metric tool. 1) Create your "empty" string signal in Formula. In the example below, we have a baseline signal that is a completely empty string and we are splicing in a string containing two spaces any time the value of the original signal is greater than 2%. You can see that the empty string signal has what looks like a constant value each time the original signal is > 2%, but when you hover the cursor over the signal you see that it is actually blank. 2) Use Formula to create a scalar threshold value to select as your threshold in Scorecard. Note, in versions R22.0.47 and greater, string values are accepted as thresholds in the scorecard metric tool, so this step can be skipped. 3) Calculate your scorecard metric. For versions before R22.0.47, the tool input for the thresholds will look like this: For versions R22.0.47 and newer, you can use: 4) The final scorecard (validated against the original one containing values):
10. i can show the value at end, which is most recent data. Is there a way add an extra column and shows weekly average, daily average in scorecard? If not, is there a way work around? I would like to see most recent and weekly average. Thank you
11. ## Scorecard Metric with Signal Name / Display Signal Name for the Maximum of 3 Signals

It is often useful to create a scorecard metric that displays a signal name for use in Organzier Topics. This is relatively simple using Formula and the toSignal() function. This Formula creates a string signal that has a value of "Signal Name" for all of time. After I have my string signal, I can use a simple scorecard metric with no statistics to create a scorecard that just displays the value of this string. I have changed the header to only display the end time. Now, I can use this scorecard in an auto-updating Organizer Topic that will always show "Signal Name". This functionality is very useful if you want to create a string signal that has more than one value. For example, say that I have three signals. I want to create a scorecard metric that tells me which of these three signals is the largest at any point in time. I will start by creating conditions for when each signal is larger than the other two. First, I use Deviation Search to find when Signal 1 > Signal 2 and for when Signal 1 > Signal 3. Then I use Composite Condition (logic: intersection), to find when Signal 1 is max. I repeat this process for Signal 2 - Deviation Search to find Signal 2 > Signal 1 and Signal 2 > Signal 3 + composite condition. Finally, I use Formula to find when Signal 3 is max by using union() and inverse() to find when Signal 1 or 2 are not max : \$1max.union(\$2max).inverse(). Now I have 3 conditions which should cover the whole time series, which are true when each Signal is the maximum of the 3 at any point! I'm now ready to create the string signal. Just like I did for a single string, I will essentially be creating new signals with toString(), but this time, I will use splice() to splice in the different strings ("1 is max", "2 is max", or "3 is max") when each condition is present. This works because my "X is max" conditions will never overlap. The result is a string signal that equals whatever signal is the maximum at every point in time! Finally, I'll use Simple Scorecard again to create a metric that displays this Max Signal for use in Organizer Topics.
12. FAQ: I have various conditions that I've created and I am trying to capture the minimum value of a signal during 1 condition and the maximum value of the same signal during another condition. I want these values displayed in a scorecard where the headers are the exact time stamps when the max or min value occurred. Solution: Let's take an example where we have two conditions for when a signal is increasing in value and when a signal is decreasing in value. We want to know the max ROC during the time periods when the signal is increasing, the min ROC when the signal is decreasing and I want them all summarized in a single scorecard metric for "max ROC". 1) Begin with the signals of interest in the display pane and conditions of interest identified. In this case we have a temperature signal, its derivative, and two conditions for when the temperature signal is increasing or decreasing in value. 2) We can use Signal from Condition to calculate the maximum ROC during each increasing time period. Make sure to specify that the time stamp of the statistic be placed at the point of max value. This will be important for displaying an accurate time header in our scorecard. 3) Use Signal from Condition again to calculate the min ROC during the decreasing time periods. This time place the time stamp of the statistic at the point of min value. 4) Use the CombineWith function in Formula to create a new signal combined of the min and max ROC value signals. 5) Use Formula to create a new condition comprised of tiny capsules for each data point in your combined min and max condition. This is done by doing a value search for when the combined ROC signal has valid data. You will notice that these capsules are so short in length that they do not render in the display pane, but you can see their start and end times, as well as the value of the combined signal during each event in the capsules pane. 6) Now switch to Scorecard view and pull the value of the combined min/max ROC signal into a condition based scorecard during the condition for when that signal has valid data. There is no need to calculate a statistic, as there is only one sample during each of these capsules. Adjust the date headers in the scorecard to display only the start or end time (as they are the same).
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