Sean T

Hi William, If you just want to make the 56 day cycle of one interval of shifts into a recurring periodic condition you can use the periods() function in formula. This function can take a number of optional arguments but the basics are: length of capsule, time between capsule starts, time to start first capsule (can be time zone adjusted). For your specific case, try this in formula: periods(56 day, 56 day, '2019-05-13') where you have a 56 day capsule that is created every 56 days starting on may 13th (i used may 13th of this year in the example)

Kareem, Do all of your temperature signals have the same threshold value? If so, we could possibly do some math to create 1/0 signals for each of your temperatures in formula and add them up giving you a total above threshold value. If each of the temperature signals has a different value, we could still potentially do it in formula, but it might be advantageous to use the value search functionality to create capsules at that point. -Sean

Jason, A little bit more context on what you are trying to accomplish would help me offer a better suggestion. From what i can gather, you are trying to splice a bunch of different values together, one value for each condition $a through $m. When none of these values are present you want the signal to be without data (i.e. invalid) is that correct? do the conditions $a through $m ever overlap? Does the calculation go on endlessly giving you the spinning wheel? or does it error out in some way? If you only try splicing together say the first 4 conditions ($a through $d) will the calculation finish loading? if a 0 value is OK when no other data is present, try replacing scalar.invalid with 0 and see if it works. Using your formula and non-overlapping conditions i was able to get this long splice chain to work by commenting out, then retyping the offending lines of code (in my case, $d and $f both error-ed out for no clear reason. In retyping them they were accepted and the formula completed.)

Use Case Background In certain use cases, it can be advantageous to move data forward or back in time. You may move signals as part of a data cleansing step in a cause and effect analysis, prior to performing a regression, or simply as a more intuitive visualization. Here are some specific examples where this might be useful: Comparing a process variable to a process setpoint when the process variable lags the setpoint. Aligning inputs and outputs in unit operations where there is a known lag or residence time: In a plug flow reactor analysis, you may want to line up the outlet flow rate with the inlet flow rates after a known residence time. In a conveyor belt analysis, you may want to compare a widget created with an input variable upstream. Aligning lab data with process data. Moving a signal in time is easily accomplished in Seeq using the Formula tool and the function .move() Moving a signal forward in time 1. Search and select the relevant signal in the data tab and click to add it to the trend. 2. In the Tools tab, select the Formula tool. 3. Give the formula an appropriate name and enter the following formula. Make sure to match the variable name in the formula tool. Here, $cp represents the Compressor Power signal I want to move. $cp.move(3h) 4. Execute the formula. You will now have a new signal where the data is shifted forward in time. Moving a signal back (earlier) in time Follow steps 1-4 above, but in step 3, use a negative sign to indicate moving the signal earlier. $cp.move(-3h) Moving a signal by the value of another signal Example use case: Oftentimes, lab data is associated with the time the lab technician completed the test and recorded the results instead of the time when the sample was obtained from the process, which could be hours or days earlier. In order to do a root cause analysis, it would be useful if the quality results lined up with when the sample was obtained. The data could be moved using a static value, like the examples above, but if there is relevant data that can be used to dynamically shift the time period based on when the sample was actually taken, that may be more accurate and useful. In this case, $labData is the lab result with the original timestamp, and $lagTime is the difference in time from when the sample is taken and when the sample result is recorded. By inputting this into the .move() function we can dynamically move the quality data to line up with the actual process conditions using the formula below. To do this, follow steps 1-4 above, but in step 3, use another signal as your first formula parameter and the second parameter representing the maximum you want to move any given sample. $labData.move($lagTime, 5min) In the above example, $labData will be moved by the value indicated in $lagTime, up to 5 minutes, the maximum any sample will be moved. Content Verified DEC2023

When performing certain types of analysis, it is desirable to combine past measured data with some future prediction, whether that prediction is dynamic or static. Future predicted data can be used for degradation or maintenance date predictions, future performance modeling, signal forecasting, or a wide variety of other potential use cases. Combining some future data with a measured signal is simple in Seeq! Another major benefit? As new data comes in the predicted values can be automatically updated with the actual data! Here is one way to join past measured data with some future forecast signal. Signals To combine measured and forecasted data we will need: Measured Data - a signal(s) that will replace the predicted signal as it becomes available Prediction / Forecast Signal - This could be a flat signal entered in formula, a signal developed in the prediction tool, or some other signal that extends out into the future Method for Combining the Signals 1. Create the Master Signal In formula, use the forecastSplice() operator to join the Measured Data and the Forecast Signal $measuredData.forecastsplice($forecastSignal) The Master Signal now appears as solid line where points are known, and dashed line where the points are still uncertain and expected to change. This is slightly different from the view shown above where the entire Master Signal was dashed due to uncertainty of future data. This helps the Seeq user to visually see where the Measured Data ends and where our Forecast Signal begins. Just as the above formula, the forecastSplice() will update as new data comes in Another operator, forecastConstant() can also be used. This would do something similar to what is shown above, however, instead of combining the Forecast Signal with the Measured Data, forecastConstant() would project the last value for the Measured Data into the future for some specified amount of time i.e. $measuredData.forecastConstant(1d) would create a Master Signal where the forecast is projected 1 day into the future: 2. Make sure the Master Signal is Auto-Updating By default when the page is loaded or the time range adjusted, the Master Signal will be recalculated and any new data from the Measured Signal will replace the Forecast Signal. Additionally, the analysis can be set to Auto Update. Content Verified DEC2023