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Bearing Damage Detection of BLDC Motors Based on Current Envelope Analysis

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This paper proposes current envelope analysis (CEA) to analyze bearing fault signals in brushless direct current (BLDC) motors, and back propagation neural networks (BPNN) to automatically identify bearing faults. We made sample motors which contained different types of fault, recorded the current signals, and extracted the current features using CEA and Hilbert Huang transform (HHT) for BPNN fault identification. The results indicate that this approach can efficiently identify bearing faults in BLDC motors.

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Sujets de la revue:
Engineering, Electrical Engineering, Control Engineering, Metrology and Testing