Overview
Do you work with operational equipment that collects sensor data? In this webinar, we will showcase an aircraft engine health example to walk through 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.
Highlights
- Organizing, visualizing, and preprocessing data
- Extracting useful features for training predictive models
- Using machine learning to detect faults and predict remaining useful life
- Deploying predictive algorithms in production systems and embedded devices
About the Presenters
Russell Graves: Russell 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.
Peeyush Pankaj: Peeyush Pankaj is a senior application engineer at MathWorks, where he has been promoting MATLAB products for data science. He works closely with customers in the areas of predictive maintenance, digital twin, enterprise integration and big data. Peeyush has 10 years of industry experience with a strong background in Aviation. Prior to joining MathWorks, he has extensively worked on aircraft engine designs, testing and certification. He has filed 25 patents on Advanced Jet Engine technologies and Prognostic Health Monitoring of aircraft engines. Peeyush holds a master’s degree in advanced mechanical engineering from the University of Sussex, UK.
