Physics-Informed Intelligent Motor Fault Detection

Abstract

Intelligent Fault Detection (IFD) has garnered significant attention, with recent advances in AI-empowered predictive maintenance. A key challenge in applying IFD models lies in the interpretability of the methods, since the mechanisms are typically complex and difficult to integrate with data-driven approaches. In addition, the integration of edge devices is an emerging trend, which ensures fault detection and subsequent decision making on the edge, and thus offering an instant response as compared to a conventional centralized server-based architecture. However, to realize Edge-based IFD the primary constraints are low storage capacity and limited computational resources. In this paper, we address various critical challenges in automatic Edge-based IFD for motors in industrial settings, focusing on three key constraints, i.e., (a) limited availability of training data, (b) the lack of method interpretability, and (c) the computational and storage limitations of edge devices. To overcome these challenges, we propose a suite of light weight Physics-Informed (PI) AI algorithms to achieve Edge-based IFD - without compromising detection performance. We validate our proposed methods on experimental data for motor fault detection, and additionally present results from the implementation of these methods on an edge device. We discuss the benefits of our proposed solutions, and give directions for future work.