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04_​Creating_​a_​ControlChart_​of_​a_​Time_​Series

Anomaly Detection. Control Chart
- Read 313 spectral amplitudes time series produced by the Pre-processing/Time Alignment & Visualization workflow - missing value replaced by last value in time series - loop on all columns to calculate 1st level alarm Training - calculate cumulative average and cumulative standard deviation Deployment - throws first level alarm if signal wanders outdside boundaries (=average +- 2* stddev) - throws second level alarm if alarm 1 averaged across all frequency bands and all signals > 0.25 Take Action - if second level alarm fires => email for mechanical check up - plot of alarm level 2 for the whole time series to see when it was the earliest moment alarm level 2 would fire. Anomaly Detection: Control ChartThis workflow detects anomalies just by checking the wandering off of the signal from a band centered around the time series "normal conditions" averageand large as 4 times the corresponding standard deviation. Node 40Node 41remove datefrom loop on columnsNode 306line plotswith date on x-axisaverage alarmon y-axislearning boundaries andthrow 1 if signal isoutside of boundariesmean(alarm 1) across signals at same timeif mean(alarm 1) > 0.25 => alarm 2Node 340resolve missingswith last value Table Creator String To Number RowID Image Writer (Port)(deprecated) Generate LinePlot 1st level alarm 2nd level alarm Fire if level2 Alarm Missing Value - Read 313 spectral amplitudes time series produced by the Pre-processing/Time Alignment & Visualization workflow - missing value replaced by last value in time series - loop on all columns to calculate 1st level alarm Training - calculate cumulative average and cumulative standard deviation Deployment - throws first level alarm if signal wanders outdside boundaries (=average +- 2* stddev) - throws second level alarm if alarm 1 averaged across all frequency bands and all signals > 0.25 Take Action - if second level alarm fires => email for mechanical check up - plot of alarm level 2 for the whole time series to see when it was the earliest moment alarm level 2 would fire. Anomaly Detection: Control ChartThis workflow detects anomalies just by checking the wandering off of the signal from a band centered around the time series "normal conditions" averageand large as 4 times the corresponding standard deviation. Node 40Node 41remove datefrom loop on columnsNode 306line plotswith date on x-axisaverage alarmon y-axislearning boundaries andthrow 1 if signal isoutside of boundariesmean(alarm 1) across signals at same timeif mean(alarm 1) > 0.25 => alarm 2Node 340resolve missingswith last value Table Creator String To Number RowID Image Writer (Port)(deprecated) Generate LinePlot 1st level alarm 2nd level alarm Fire if level2 Alarm Missing Value

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On NodePit since: 2021-04-27
Last update: 2024-12-17
Created with KNIME version: v4.3.1
Tags: anomaly detectiontime series analysisauto-regressive modelstime alignmentIoTInternet of Things

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