Live Events

Identifying Motor Faults using Machine Learning for Predictive Maintenance


Overview

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

Highlights include:

  • Accessing and exploring large datasets
  • Interactively extracting and ranking features
  • Training machine learning algorithms
  • Generating synthetic data from models
  • Deploying algorithms in operation

Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.

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.

Product Focus

Identifying Faults in Electric Motors with Predictive Maintenance

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