Feature Extractor

the feature extractor processor extracts meaningful patterns and characteristics from raw plc data, transforming complex data into features that advanced analytic models can easily understand and utilize feature extractor overview let's say you picked a window of n values minimum gives you minimum of n values maximum gives you maximum of n values average gives you the average(mean) of n values standard deviation squareroot( (x average)^2/(n 1) ) variance (x average)^2/(n 1) median n/2 for odd number of window elements, {\[n 1] + \[n]}/2 for even window elements kurtosis \[ (n)(n+1) / (n 1)(n 2)(n 3) ] sigma \[(x i x avg)/(stddeviation) ^4] skewness \[ (n) / (n 1)(n 2)] sigma \[(x i x avg)/(stddeviation) ^3] zero crossing rate gives you how many times the signal has crossed zero i e positive value to negative, or negative value to positive root mean square squareroot\[ sigma x^2 / n] quartiles divides the signal into 4 equal quartiles, based on medians inter quartile range difference between the third quartile and first quartile mean absolute deviation abs(x average)/(n) average absolute variation abs(x average)/(n) the timer interval parameter is useful if the connected input tag is currently not polling, but you still want this kpi to publish a value every few seconds, defined by the aforementioned timer if you know your input is going to publish at the expected interval, it is better to disable this timer by entering 0 in the field feature extractor parameters parameters details window size this parameter specifies the window in which to calculate the features, represented in seconds minimum maximum average standard deviation variance median kurtosis skewness zero crossing rate root mean square quartiles inter quartile range mean absolute deviation average absolute variation minimum it gives the minimum value within the specified window size maximum this parameter gives you the maximum value within the specified window size average it calculates the average (mean) of the values within the window size standard deviation this parameter computes the standard deviation of the values within the window variance it calculates the variance of the values within the window median this parameter gives you the median of the values within the window kurtosis kurtosis measures the "heaviness" of the tails of the distribution of values within the window skewness skewness measures the asymmetry of the distribution of values within the window zero crossing rate it determines the number of times the signal crosses zero within the window root mean square this parameter calculates the root mean square of the values within the window quartiles it divides the signal into four equal quartiles based on the medians within the window inter quartile range this parameter determines the difference between the third quartile and the first quartile within the window mean absolute deviation it calculates the mean absolute deviation of the values within the window average absolute variation this parameter calculates the average absolute variation of the values within the window timer interval this parameter allows you to set the timer in milliseconds it's useful for continuous output display when no event is triggering the expression pass through value the pass through value parameter enables you to specify how anomalous data should be handled note when creating an analytics flow with feature extractor processor, refer the docid\ qhbt7woo9l9qbklo7gbtp guide for more details