Use the Signal Decomposition Function

you can use the signal decomposition function to extract and separate signal components from composite signals, which are related to semantic units this function decomposes signals by computing trends through linear regression and seasonality through naive differencing user scenario review the following scenario for the signal decomposition function then, you will simulate plc data and calculate the trend in the data in a wind farm, the signal decomposition function is utilized to analyze the output from turbines it helps to separate the consistent patterns due to seasonal wind changes from the overall trends in turbine performance this information is crucial for scheduling maintenance during expected low wind periods and optimizing energy production throughout the year step 1 add a device follow the steps to connect a device docid\ ish7bqhzxswtdx8vbnszb and configure the following parameters device type simulator driver name generator enable alias topics select the checkbox step 2 add tags after connecting the device, add the following tag see add tags docid\ xgwokqbtpevii7or82ll0 to learn more tag 1 input1 name select s random value generator value type select float64 polling interval enter 1 tag name enter input1 min value enter 101 max value enter 199 step 3 create analytics flows you can now create the analytics flows using data from the device and tag you previously created to create an analytics flow with the signal decomposition processor function in litmus edge, navigate to analytics on the analytics canvas, click add processor the create a processor dialog box displays select datahub subscribe in the topic field, click the search icon, select the device you previously created, and then select the alias topic for the input1 tag click save click add processor again and select the signal decomposition processor the edit a processor dialog box appears window size enter a value that represents the range to apply the signal decomposition function for this example, we input a value of 10 model type select between additive and multiplicative additive model signal = trend + seasonality + residue multiplicative model signal = trend seasonality residue periodicity enter a value to determine how frequently the observations are spaced in time for this example, we input a value of 5 click save connect the datahub subscribe processor (tag input1 ) to the signal decomposition processor with a wire and use the events connection on the analytics canvas, click save the configured analytics flows should look like the following step 4 view output of processor click the view icon in the signal decomposition processor to view the output values the output shows the data with the trend removed at 166 05 , a seasonal component of 131 78 , and a positive trend in the data at 26 16 ​