A Survey on Machine Learning Applied to Dynamic Physical Systems

TL;DR

This survey reviews recent progress in applying machine learning techniques to model, control, and detect faults in nonlinear physical systems like electric motors, highlighting advancements and ongoing challenges.

Contribution

It provides a comprehensive overview of machine learning applications in modeling and fault detection for electric motors, emphasizing recent developments.

Findings

Machine learning enhances modeling accuracy for nonlinear systems.

Improved fault detection methods for electric motors using ML.

Survey identifies key challenges and future directions in the field.

Abstract

This survey is on recent advancements in the intersection of physical modeling and machine learning. We focus on the modeling of nonlinear systems which are closer to electric motors. Survey on motor control and fault detection in operation of electric motors has been done.