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 true false 182false unhandled content type false unhandled content type false unhandled content type false unhandled content type false unhandled content type false unhandled content type false unhandled content type false unhandled content type false unhandled content type false unhandled content type note when creating an analytics flow with feature extractor processor, refer the use the feature extractor function docid km9oy9g1safbselm9ziv guide for more details