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This paper proposes a method to diagnose the open-circuit faulty phases and faulty points of the six-phase permanent magnet synchronous motor (PMSM) drive circuit. The current sensor is used to obtain the six-phase current signal, and the least mean square error (LMS) adaptive filtering algorithm is used to filter out the vibration and noise. Empirical Mode Decomposition (EMD) is performed on the filtered current signals, and the EMD energy entropy of each phase current signal is calculated. The change of energy entropy can simplify the double-bridge arm open-circuit fault to the single-bridge arm open-circuit fault, which reduces the number of fault characteristics. After the faulty phase is judged by the change of energy entropy, the fault diagnosis system can diagnose the specific faulty point according to the normalized average value of each phase current signal. Finally, the current data of normal state and fault state are used to train the support vector machine (SVM) and classify the fault state. Each type of fault can be accurately diagnosed by substituting experimental data into the SVM. Experimental results prove that the proposed method can accurately diagnose the open-circuit faults of the six-phase PMSM drive system.

Open-Circuit Fault Diagnosis of Six-Phase Permanent Magnet Synchronous Motor Drive System Based on Empirical Mode Decomposition Energy Entropy