Many companies are trying to reduce unexpected failures in their equipment by developing predictive maintenance algorithms. Some of the difficulties associated with developing these algorithms include the lack of failure data from the machine, as well as being unable to incorporate their engineering and modeling expertise into the final solution.
In this webinar, we will show you how to use physical models of your system to generate failure data by simulating fault conditions, and then using this data to train a predictive model. We also show you how to integrate these algorithms with your embedded devices so that your algorithms operate directly on real data coming from your machine.
About the Presenters
Terry Denery is an expert in modeling. He runs hundreds, if not thousands, of simulations to pursue the best design of electrical, mechanical, and control systems.
Terri Xiao joined MathWorks in January 2009 as a Senior Application Engineer. She covers the Simulink product family with focus on physical modeling and control tools. Her previous work experience includes four years at SySense Inc, a consulting company that specializes in guidance, navigation, and control systems for the aerospace industry. Terri received her B.S. and M.S. in Mechanical Engineering from The University of California, Los Angeles. While pursuing her graduate studies, her concentration was in Systems and Controls.
Recorded: 25 Apr 2017
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