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A typical data cleansing workflow is to exclude equipment downtime data from calculations. This is easily done using the .remove() and .within() functions in Seeq formula. These functions remove or retain data when capsules are present in the condition that the user supplies as a parameter to the function. There is a distinct difference in the behavior of the .remove() and .within() functions that users should know about, so that they can use the best approach for their use case. .remove() removes the data during the capsules in the input parameter condition. For step or linearly interpolated signals, interpolation will occur across those data gaps that are of shorter duration than the signal's maximum interpolation. (See Interpolation for more details concerning maximum interpolation.) .within() produces data gaps between the input parameter capsules. No interpolation will occur across data gaps (no matter what the maximum interpolation value is). Let's show this behavior with an example (see the first screenshot below, Data Cleansed Signal Trends), where an Equipment Down condition is identified with a simple Value Search for when Equipment Feedrate is < 500 lb/min. We then generate cleansed Feedrate signals which will only have data when the equipment is running. We do this 2 ways to show the different behaviors of the .remove() and .within() functions. $Feedrate.remove($EquipmentDown) interpolates across the downtime gaps because the gap durations are all less than the 40 hour max interpolation setting. $Feedrate.within($EquipmentDown.inverse()) does NOT interpolate across the downtime gaps. In the majority of cases, this result is more in line with what the user expects. As shown below, there is a noticeable visual difference in the trend results. Gaps are present in the green signal produced using the .within() function, wherever there is an Equipment Down capsule. A more significant difference is that depending on the nature of the data, the statistical calculation results for time weighted values like averages and standard deviations, can be very different. This is shown in the simple table (Signal Averages over the 4 Hour Time Period, second screenshot below). The effect of time weighting the very low, interpolated values across the Equipment Down capsules when averaging the Feedrate.remove($EquipmentDown) signal, gives a much lower average value compared to that for $Feedrate.within($EquipmentDown.inverse()) (1445 versus 1907). Data Cleansed Signal Trends Signal Averages over the 4 Hour Time Period Content Verified DEC2023
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!