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The paper presents methodologies to detect and locate short-circuit faults on the stator winding of VSI-fed PM brushless dc motors. Normal performance characteristics of the motor are obtained through a discrete-time lumped-parameter network model. The model is modified to accommodate shortcircuit faults in order to simulate faulty operation. Fault signatures are extracted from the waveforms of electromagnetic torque and phase-voltage summation using wavelet transform. Three independent detection techniques are introduced. Experimental measurements agree acceptably with simulation results, and validate the proposed methods. This work sets forth the fundamentals of an automatic fault detector and locator, which can be used in a fault-tolerant drive.Index Terms-Brushless dc motor, fault detection, wavelet transform.

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The paper presents a neuro-fuzzy-based perspective to the automation of diagnosis and location of stator-winding interturn short circuits in CSI-fed brushless dc motors. Performance of the drive under normal and short-circuit conditions are obtained through classical lumped-parameter network models. Waveforms of the electromagnetic torque and summation of phase voltages are monitored to develop two independent diagnostic algorithms. Diagnostic indices derived from the characteristic waveforms using discrete Fourier transform (DFT) lead to identifying the number of shorted turns. Fault location is achieved through a different set of indices extracted by the short-time Fourier transform (STFT). Adaptive neuro-fuzzy inference systems (ANFIS)are trained based on simulation results to automate the diagnostic process. ANFIS testing along with the good agreement between simulated and measured waveforms show the effectiveness of the proposed techniques.

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