A Novel Induction Machine Fault Detector Based on Hypothesis Testing

This paper investigates a new fault detection method for induction machines diagnosis. The proposed detection method is based on hypothesis testing. The decision is made between two hypotheses: the machine is healthy and the machine is faulty. The generalized likelihood ratio test is used to address this issue with unknown signal and noise parameters. To implement this detector, the unknown parameters are replaced by their estimates. Specifically, four estimations are required, which are model order, frequency, phase, and amplitude estimations. The model order is obtained using the Bayesian information criterion. Total least-squares estimation of signal parameters via rotational invariance techniques is used to estimate frequencies. Then, phases and amplitudes are obtained using the least-squares estimator. The proposed approach performance is assessed using simulation data by plotting the receiver operating characteristic curves. Two faults are considered: bearing and broken rotor bar faults. Experimental tests clearly show the effectiveness of the proposed detector.

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