The problem of fault diagnosis in electrical motors has an important impact on the supervision of dynamic systems, and model-based methods are efficient tools for this purpose. In this regard, this work presents a novel method for detecting inter-turn short circuits (ITSCs) in the stator windings of permanent magnet synchronous motors (PMSMs). The approach is based on algebraic identification to process the motor voltage signals, estimating the offsets, amplitudes, and phases of the fundamental and third-harmonic components. Fault detection is performed in two steps: first, a voltage imbalance index is evaluated to determine the presence of abnormal operating conditions. Subsequently, characteristic patterns in the estimated parameters are analyzed to identify both the fault type and the affected phase(s). The experimental results show that single-phase ITSC faults produce a reduction in the offset of the faulted phase together with an increase in its third-harmonic amplitude, whereas phase-to-phase ITSC faults lead to an increase in the offsets of the affected phases and nearly identical third-harmonic amplitudes between them. In both cases, only minor variations are observed in the estimated phase angles. The effectiveness of the proposed methodology is supported through theoretical analysis and validated experimentally using voltage measurements acquired from a PMSM test bench. The results demonstrate that the proposed technique can accurately identify fault conditions through voltage imbalance and harmonic-pattern analysis, providing a practical and computationally efficient methodology for PMSM stator winding fault diagnosis.
Marcos-Andrade et al. (Wed,) studied this question.
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