|26 Oct 2021||5:30 AM EDT|
|26 Oct 2021||9:00 AM EDT|
|26 Oct 2021||2:00 PM EDT|
Do you work with operational equipment that collects sensor data? In this seminar, you will learn how you can utilize that data for Predictive Maintenance, the intelligent health monitoring of systems to avoid future equipment failure. Rather than following a traditional maintenance timeline, predictive maintenance schedules are determined by analytic algorithms and data from sensors. 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.
- Accessing and preprocessing data from a variety of sources
- Using machine learning to develop predictive models
- Creating dashboards for visualizing and interacting with model results
- Deploying predictive algorithms in production systems and embedded devices
- Using simulation to generate data for expensive or hard-to-reproduce failures
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
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 and is a late-stage mechanical engineering doctoral candidate.
Rachel Johnson is the Product Manager for Predictive Maintenance Toolbox at MathWorks. Previously, she was a Senior Application Engineer supporting the Aerospace and Defense Industry. Rachel spent her pre-MathWorks days at the Office of Naval Intelligence where she used MATLAB and Simulink for missile analysis and simulation. She has also taught high school math, physics, and engineering. Rachel holds a B.S.E. in Aerospace Engineering from Princeton University, an M.S. in Aerospace Engineering from the University of Maryland, and an M.A.T. in Mathematics Education from Tufts University.