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.
- 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.