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01_​Preprocessing_​Time_​Alignment_​and_​Visualization

Anomaly Detection. Time Alignment & Visualization
Visualization Techniques Open metanode to read andpreprocess files in the _datafolderFiles are quite big and execution of this metanode can be slow on a non-powerful machine. This workflow preprocesses and visualizes sensor data for anomaly detection Read the original 28data files with FFT resultsamplitude, frequency, timecorrelation mapof a sample with its pastcorrelation map of a sample with samples at the same timefrom other seriesScatter Matrix3 colored time windowsline plotsfrequency vs. timeheatmapReadAlignedData.csvDates Read all data Auto-correlationmaps Correlation Maps Scatter Matrix Line Plot Heatmap CSV Reader String to Date&Time Visualization Techniques Open metanode to read andpreprocess files in the _datafolderFiles are quite big and execution of this metanode can be slow on a non-powerful machine. This workflow preprocesses and visualizes sensor data for anomaly detection Read the original 28data files with FFT resultsamplitude, frequency, timecorrelation mapof a sample with its pastcorrelation map of a sample with samples at the same timefrom other seriesScatter Matrix3 colored time windowsline plotsfrequency vs. timeheatmapReadAlignedData.csvDates Read all data Auto-correlationmaps Correlation Maps Scatter Matrix Line Plot Heatmap CSV Reader String to Date&Time

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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: 2019-07-12
Last update: 2024-04-20
Created with KNIME version: v4.3.2
Tags: anomaly detectiontime series analysistime alignmentIoTInternet of Thingsscatter matrixcorrelation matrixpredictive maintenancesensor

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