Permanent Magnet Synchronous Motor Driving Mechanical Transmission Fault Detection and Identification: A Model-Based Diagnosis Approach

This paper presents a model-based scheme for permanent magnet synchronous motor (PMSM) driving transmission fault detection and identification (FDI) in a steady-state condition. The proposed framework utilizes a PMSM state-space model and an approximated transmission model to construct the regression models for parameter estimation using the Recursive Least-Square (RLS) algorithm. The FDI are accomplished by the residual current spectrum thresholding method to assess the fault characteristic frequency magnitude and also by parameter clustering. Two types of mechanical transmission with three different fault conditions are tested in the experiments. As a preliminary effort in the condition monitoring of PMSM driving transmission, the study results demonstrate a promising approach by considering both residual current spectrum and parameter cluster, which achieved a satisfactory decision making in detecting and identifying the faulty condition.