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Machine Learning for Electrical Signature Analysis and Remaining Useful Life

Howard Penrose | President, MotorDoc LLC
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Summary

Electrical Signature Analysis and Motor Current Signature Analysis perform very well as expert systems for fault detection. In this presentation we will discuss the application of machine learning predictive analytics as part of data collection and continuous monitoring with ESA. Attendees will have a basic understanding of supervised machine learning systems and how they are applied to identify defects and time to failure estimation methods for electric machines, power quality and driven equipment using the motor or generator as the sensor.

About the Presenter

Howard is the President of MotorDoc® LLC and the 2018 Chair of SMRP. He has over 35 years of electric motor testing, repair and design experience, starting with a US Navy motor repair job to advanced electric machinery design. Howard is also involved in legislation with the US Government regarding Cyber Security, Infrastructure, Energy, SmartGrid Education and Safety.

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About the Author

Howard Penrose President, MotorDoc LLC

Howard is the President of MotorDoc® LLC and the 2018 Chair of SMRP. He has over 35 years of electric motor testing, repair and design experience, starting with a US Navy motor repair job to advanced electric machinery design. Howard is also involved in legislation with the US Government regarding Cyber Security, Infrastructure, Energy, SmartGrid Education and Safety.

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