Diagnosis and Classification of Short-Circuit Faults and Current Sensor Gain Errors in PMSM Drives Based on High-Frequency Voltage Disturbances

Fault diagnosis of permanent magnet synchronous motors (PMSMs) is often challenged by high misdiagnosis rates when stator short-circuit faults (SSCF) and current sensor gain errors (CSGE) present similar fault signatures. Aiming to deal with such a challenge in real time, this article proposes an integrated fault diagnosis, localization, and classification framework based on high-frequency (HF) voltage disturbances. First, a unified SSCF model across different fault phases is established, along with a model for CSGE. Secondly, a generalized integrator extended state observer (GIESO) is adopted to extract HF voltage disturbances with enhanced resolution. And then, a lightweight localization algorithm based on multi-phase disturbance amplitude differences that avoids additional sensors or complex matrix operations is proposed. Furthermore, a rapid fault classification is achieved by exploiting unique HF spectral components of CSGE versus SSCF. In addition, an adaptive gain correction mechanism can mitigate the masking effect of CSGE on SSCF under composite faults. Experimental results have confirmed real-time deployability on standard DSP platforms. And the proposed online fault diagnosis and classification method can achieve superior response speed, localization and classification accuracy, and robustness to operating condition variations.