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.
Interturn short circuits are among the most critical faults in permanent magnet synchronous motor drives, as they combine localized heating in the shorted stator phase with electrical asymmetry that distorts the current feedback used for torque-producing control. This article proposes a control-based mitigation method...
L. Zezula, M. Kozovský, Ludek Buchta et al.· arXiv.org· 0 citations
Fault detection is one of the most common studies on wind turbines. In this case, the doubly fed induction generator (DFIG) is specifically analyzed. The fault cases analyzed are: inter-turn short circuit and open circuit, in addition to normal operating conditions. The K-means algorithm was used for analysis and class...
Anthony Molina, A. Romero, G. Suvire· Simposio Internacional sobre...· 0 citations
applications, brushless DC (BLDC) motor drives are becoming more and more significant because of their dependability, efficiency, and flexibility with regard to renewable energy systems (RESs). In this investigation, a novel fault detection technique for three-phase inverter (3PhI) switch resistance fault of brushless...
Results showed that the DSP-based online condition monitoring system was more accurate and better at detecting faults than earlier methods, making it a good fit for usage in industrial applications.
Mohan P. Thakre, Badal Kumar, Supriya Nilesh Thakur et al.· Bulletin of Electrical Engin...· 0 citations
Induction machine fault diagnosis using current spectral analysis is a well-established diagnostic technique based on the identification of the characteristic harmonic components generated in the machine current by each type of fault. However, one of the main problems with the application of this technique to the diagn...
J. Martínez-Román, R. Puche-Panadero, Carla Terron-Santiago et al.· IEEE Transactions on Instrum...· 0 citations
The outcomes prove that the integrated technical framework (time-domain features + FR + SVM) provides zero false alarms and a balanced diagnostic system that combines computational speed with high precision.
H. Zaimen, T. Thelaidjia, Makhlouf Chouki et al.· International Journal of Ele...· 0 citations
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