Machine Learning for Electrical Signature Analysis and Remaining Useful Life

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.

• Howard Penrose
• MotorDoc LLC