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Diagnosis of Open-Phase Faults in PM Brushless DC Motors Using Wavelet and Adaptive Fuzzy Techniques

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A discrete-time model is used to simulate performance of PM brushless DC motor drives under normal and faulty operations. Normal conditions included perfect and imperfect commutations as well as noisy operation. Faulty operation was considered an open-circuit fault on one phase of the stator windings. Current waveform of the motor-DC link is monitored and processed using continuous wavelet transform to derive suitable diagnostic indices. An adaptive neuro-fuzzy inference system (ANFIS) is trained based on indices extracted under various operating conditions in order to automate the diagnosis process. The developed ANFIS yielded a perfect diagnosis of the fault. A good agreement between simulation and experimental results is achieved.
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Keywords: brushless DC motors; fault diagnosis; neuro-fuzzy systems; wavelet transform

Document Type: Research Article

Affiliations: Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA

Publication date: 01 November 2004

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