Identifying Motor Faults using Machine Learning for Predictive Maintenance скачать в хорошем качестве

Identifying Motor Faults using Machine Learning for Predictive Maintenance

Do you want to identify faults in equipment using sensor data? In this webinar, you will learn how to build data-driven fault detection algorithms for induction motors – even if you aren’t a machine learning expert. Starting with a dataset collected from motor hardware, we will walk through the end-to-end process of developing a predictive maintenance algorithm. Highlights: Accessing and exploring large datasets Interactively extracting and ranking features Training machine learning algorithms Generating synthetic data from models Deploying algorithms in operation Check out other Predictive Maintenance examples: https://bit.ly/PdM-Examples About the Presenters: Dakai Hu joined MathWorks’ Application Engineering Group in 2015. He mainly supports automotive engineers in North America working on electrification. His area of expertise includes e-motor drives control system design, physical modeling, and model-based calibration workflows. Before joining MathWorks, Dakai earned his Ph.D in electrical engineering from The Ohio State University, in 2014, where he published 5 first-author IEEE conference and transaction papers in the area of traction e-motor modeling and controls. Shyam Keshavmurthy is an Application Engineer who focuses on digital twins and AI. He has been at MathWorks for 3 years, and has 20+ years of experience in applying AI for quality and operational data. He has a Ph.D. in Nuclear Engineering and Computer Science. 00:00 Introduction 02:24 Why Do Predictive Maintenance? 05:27 Predictive Maintenance Workflow 07:00 Problem Definition: Broken Rotor Bar Faults 08:04 Accessing Large Datasets 08:52 Example: Broken Rotor Fault Detection Example 10:02 Accessing and Organizing Out-of-Memory Data with File Ensemble Datastore 13:33 Band Pass Filter Design 16:20 Processing Data using Diagnostic Feature Designer 20:23 Generating Time and Frequency Domain Features using Diagnostic Feature Designer 26:18 Training Machine Learning Models using Classification Learner 31:50 Machine Learning Model Deployment 35:45 Summary #predictivemaintenance -------------------------------------------------------------------------------------------------------- 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 © 2023 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.