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02_​Time_​Series_​AR_​Training

Anomaly Detection. Time Series AR Training

This workflow trains an auto-regressive model to predict signal values. Only time series values from normal functioning conditions are used to train the model.

- Read 313 spectral amplitudes time series produced by the Pre-processing/Time Alignment & Visualization workflow - define training set with only normal data (till August 2007) - For each frequency bin, for each sensor, for each motor part: - train auto-regressive model on training set (Lag = 10) - write PMML model (313 models) Anomaly Detection: Time Series AR TrainingThis workflow trains an auto-regressive model to predict signal values. Only time series valuesfrom normal functioning conditions are used to train the model. Learn "normality" with a linear auto-regressive modelsfor each partfor each sensorfor each frequency bandNode 324 Learn "normal" Read IOTTraining Data - Read 313 spectral amplitudes time series produced by the Pre-processing/Time Alignment & Visualization workflow - define training set with only normal data (till August 2007) - For each frequency bin, for each sensor, for each motor part: - train auto-regressive model on training set (Lag = 10) - write PMML model (313 models) Anomaly Detection: Time Series AR TrainingThis workflow trains an auto-regressive model to predict signal values. Only time series valuesfrom normal functioning conditions are used to train the model. Learn "normality" with a linear auto-regressive modelsfor each partfor each sensorfor each frequency bandNode 324 Learn "normal" Read IOTTraining Data

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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On NodePit since: 2019-10-03
Last update: 2024-04-18
Created with KNIME version: v4.0.2

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