Broken magnets fault detection in pmsm using a convolutional neural network and svm

Abstract

The Permanent Magnet Synchronous Motor (PMSM) stands as a pivotal component in various applications, yet it remains susceptible to an array of faults within both its rotor and stator, there arises an imperative to swiftly and intelligently address these issues. In this study, a novel approach was undertaken wherein a PMSM design was conceptualized within the Ansys Maxwell program, followed by the deliberate introduction of a fault at the rotor's magnetic level. Specifically, three distinct fault scenarios were delineated based on the number of broken magnets (BM), namely 2, 3, and 4, localized within specific rotor areas. Notably, the magnetic flux density was selected as the focal parameter for this investigation. To effectively detect and diagnose faults stemming from BM, an innovative Convolutional Neural Network (CNN) architecture was devised. Leveraging images of the PMSM design captured during operational phases at various time intervals, the CNN exhibited remarkable efficacy in discerning and categorizing fault instances. Upon analysis of the derived outcomes, it becomes evident that the CNN exhibited unparalleled accuracy in fault detection, achieving a remarkable 100% success rate when juxtaposed with alternative methodologies such as Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), which yielded accuracy rates of 97%.