Machine Learning for Inverter-Fed Motors Monitoring and Fault Detection: An Overview

Abstract

Monitoring and fault detection can be critical for efficient, safe and reliable operation of electric drive systems. Unfortunately, developing accurate physics-based models for these tasks is difficult due to unknown machine parameters and incomplete knowledge of the physical phenomena occurring within the system. Machine Learning (ML) methods can learn the system’s behavior from data without requiring explicit models. However, expert knowledge of the system is still crucial to extract useful features before applying ML models. This paper presents an overview of the use of ML and data visualization methods for condition monitoring of inverter fed induction motors. More specifically, stator winding temperature estimation and insulation degradation are considered. The analyzed methods make use of the signals normally available in electric drives. Time and frequency-based approaches are considered. The developed methods are assessed on an experimental test bench. The paper is intended to bridge ML and electric drive domains. The desired outcome of this work is to provide useful guidelines for researchers in the electric drives field who aim to apply modern ML and data visualization techniques for monitoring and fault detection.