Predictive Maintenance, Part 4: How to Use Diagnostic Feature Designer For Feature Exraction
Learn how you can extract time-domain and spectral features using Diagnostic Feature Designer for developing your predictive maintenance algorithm.
- Overcoming Four Common Obstacles to Predictive Maintenance: http://bit.ly/2GoZjyI
There are hundreds of features you can extract from your data. How do you know which features are useful for training a machine learning model? Although these models can work with a high-dimensional set of features, these features need to be distinctive so the model can make accurate predictions and effectively separate different types of groups. In this video, we’ll discuss how you can extract useful features with the Diagnostic Feature Designer for a triplex pump and train machine learning models with Classification Learner for fault classification.
- Feature Extraction Using Diagnostic Feature Designer App: http://bit.ly/2HVohWv
- To replicate the steps discussed in the video, check out this example: http://bit.ly/2HVoLfh
- MATLAB and Simulink for Predictive Maintenance: http://bit.ly/2E5LRgh
- Overcoming Four Common Obstacles to Predictive Maintenance: http://bit.ly/2GoZjyI
There are hundreds of features you can extract from your data. How do you know which features are useful for training a machine learning model? Although these models can work with a high-dimensional set of features, these features need to be distinctive so the model can make accurate predictions and effectively separate different types of groups. In this video, we’ll discuss how you can extract useful features with the Diagnostic Feature Designer for a triplex pump and train machine learning models with Classification Learner for fault classification.
- Feature Extraction Using Diagnostic Feature Designer App: http://bit.ly/2HVohWv
- To replicate the steps discussed in the video, check out this example: http://bit.ly/2HVoLfh
- MATLAB and Simulink for Predictive Maintenance: http://bit.ly/2E5LRgh
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