Diagnosis of rotor demagnetization and eccentricity faults for IPMSM based on deep CNN and image recognition

Abstract

Permanent magnet synchronous motors (PMSMs) have many advantages attributed to the structure of the permanent magnet, which connects the influences of each type of fault. However, diagnostic methods are not capable of monitoring multitype faults with one index or method, due to closed-loop control or non-stationarity. There are a few diagnostic methods based on convolutional neural networks (CNNs) and vibration analysis; however, they are limited by the installed sensor locations and are affected by various factors of the driven system. This paper proposes a method to diagnose eccentricity and demagnetization in an interior PMSM (IPMSM) with image recognition based on a deep CNN and motor stator current analysis. To extract fault features from the IPMSM’s stator current with a deep CNN, a gray image transformation algorithm that uses the autocorrelation matrix is proposed to enhance the feature representations of stator currents, in which currents data are processed recursively and timely. The testing accuracy of 98.74% of both designed model pyramidal Resnet-9 and Resnet-15 indicates that the proposed method is capable of monitoring multitype faults and is immune to speeds and loads.