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: 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
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