Feature Extraction Using Diagnostic Feature Designer App
Use Diagnostic Feature Designer app to extract time-domain and spectral features from your data to design predictive maintenance algorithms.
In this example, measurements have been collected from a triplex pump under different fault conditions. The app lets you import this data and interactively visualize it. You can group the measurements by different fault conditions. After the time-domain and spectral features are extracted from the data, you can evaluate the effectiveness of the extracted features using histograms. You can also rank them to determine numerically which features are likely to best discriminate healthy and faulty behavior. Finally, the most effective features are exported to Classification Learner app for further evaluation of feature effectiveness and for training machine learning models.
Other Resources
MATLAB and Simulink for Predictive Maintenance: http://bit.ly/2E5LRgh
MATLAB Tech Talks on Predictive Maintenance (5 Videos): http://bit.ly/2IomYgs
Analyze and Select Features for Pump Diagnostics: http://bit.ly/2MA6nvv
In this example, measurements have been collected from a triplex pump under different fault conditions. The app lets you import this data and interactively visualize it. You can group the measurements by different fault conditions. After the time-domain and spectral features are extracted from the data, you can evaluate the effectiveness of the extracted features using histograms. You can also rank them to determine numerically which features are likely to best discriminate healthy and faulty behavior. Finally, the most effective features are exported to Classification Learner app for further evaluation of feature effectiveness and for training machine learning models.
Other Resources
MATLAB and Simulink for Predictive Maintenance: http://bit.ly/2E5LRgh
MATLAB Tech Talks on Predictive Maintenance (5 Videos): http://bit.ly/2IomYgs
Analyze and Select Features for Pump Diagnostics: http://bit.ly/2MA6nvv
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