Feature Extraction Using Diagnostic Feature Designer | Predictive Maintenance

Learn how you can extract time-domain and spectral features using Diagnostic Feature Designer for developing your predictive maintenance algorithm. 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. Related Resources: To replicate the steps discussed in the video, check out this example: https://bit.ly/2HVoLfh MATLAB and Simulink for Predictive Maintenance: https://bit.ly/2Tp2yLq Overcoming Four Common Obstacles to Predictive Maintenance: https://bit.ly/2GoZjyI Download Ebook: Introduction to Predictive Maintenance with MATLAB: https://bit.ly/2GQw0TL -------------------------------------------------------------------------------------------------------- Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2019 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.