Induction machine faults detection based on a constant false alarm rate detector

This paper presents a novel approach for induction machine condition monitoring using stator current measurements. The proposed method, based on hypothesis testing, specifically investigates a binary detection problem: the machine is healthy or faulty. The Generalized Likelihood Ratio Test (GLRT) is used to address this statistical detection problem with unknown signal and noise parameters. It is indeed a Constant False Alarm Rate (CFAR) detector. Decision is obtained according to a threshold, which is set to reach a desired false alarm probability. The proposed detector implementation needs estimations that are based on the Maximum Likelihood Estimator (MLE). In particular, Total Least Squares-Estimation of Signal Parameters via Rotational Invariance Techniques (TLS-ESPRIT) estimates frequencies. The proposed CFAR detector is tested on experimental data of bearings faults and broken rotor bars that clearly show it effectiveness.

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