Incipient Interturn Short-Circuit Fault Diagnosis of Permanent Magnet Motors Based on Multiscale Entropy and Topological Data Analysis

Abstract

Incipient stator interturn short-circuit faults in permanent magnet synchronous motors produce only weak changes in the terminal currents, which limits the sensitivity of conventional amplitude- and unbalance-based indicators. This paper proposes a phase-wise diagnostic framework that combines multiscale sample entropy (MSE), topological data analysis (TDA), and a Gaussian mixture model (GMM). For each three-period current window, ten scale-dependent sample-entropy components and two persistent-entropy components are concatenated into a 12-dimensional feature vector. A separate GMM is trained for each phase using healthy data only. The resulting likelihood-based health scores are used for fault detection and faulty-phase localization, while physically defined score boundaries calibrated from measured short-circuit-current groups are used for severity assessment. Experiments on a 1.5 kW, 8-pole, 12-slot PMSM demonstrate class-wise recalls of 96.50–100% and an overall accuracy of 97.50% under the investigated operating conditions. The results show that the combined temporal and topological representation can reveal weak current changes that are difficult to distinguish using conventional terminal-current indicators.