Study on Inter-turn Short Circuit Fault Diagnosis Methods for AC Permanent Magnet Synchronous Motors

Permanent Magnet Synchronous Motors (PMSMs) are critical components in industrial applications, making accurate fault diagnosis essential for system health management. This paper focuses on the early diagnosis of inter-turn short circuit (ITSC) faults. A fault simulation model is established in the d-q coordinate system based on the mathematical principles of PMSMs, from which the q-axis current, voltage, torque, and speed signals are extracted as potential features. A hybrid RF-OOB-LDA-RF diagnostic framework is proposed: time-domain, frequency-domain, and time-frequency features are first extracted from simulation data under noisy conditions. A Random Forest (RF) algorithm, integrated with Out-of-Bag (OOB) estimation and Grid Search, is then employed for optimal feature selection. Linear Discriminant Analysis (LDA) further reduces the dimensionality of the selected feature set, which is finally fed into another RF classifier. This approach enhances both diagnostic accuracy and model generalization. Experimental results from a physical motor simulation platform validate the method’s effectiveness and feasibility for real-world systems, providing crucial technical support for PMSM health management.