Predictive Maintenance with MATLAB and Simulink: From Data to Deployment

Overview

Do you work with operational equipment that collects time series sensor data? In this seminar, you will learn how you can utilize that data to design features and train predictive maintenance algorithms to identify faults and estimate remaining useful life in MATLAB. With predictive maintenance, organizations can identify issues before equipment fails, pinpoint the root cause of the failure, and schedule maintenance as soon as it’s needed.

Highlights

What is predictive maintenance?
Overview of the predictive maintenance algorithm development workflow
Interactively extracting, visualizing, and ranking features using the Diagnostic Feature Designer
Using low-code machine learning to develop predictive algorithms
Using simulation to generate data for expensive or hard-to-reproduce faults
Deploying algorithms to the edge without writing additional code

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 Presenter

Russell Graves is an Application Engineer at MathWorks focused on machine learning and systems engineering. Prior to joining MathWorks, Russell worked with the University of Tennessee and Oak Ridge National Laboratory in intelligent transportation systems research with a focus on multi-agent machine learning and complex systems controls. Russell holds a B.S. and M.S. in Mechanical Engineering from The University of Tennessee.

Product Focus

Predictive Maintenance with MATLAB and Simulink: From Data to Deployment

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