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

International audienceThis paper proposes a new method for fault detection in three-phase induction machines based on the stator current measurements. The proposed detection method is based on the hypothesis testing. Specifically, this paper 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. This proposed detector is a Constant False Alarm Rate (CFAR) detector. The decision is obtained according to a threshold, which is set to reach a desired false alarm probability. To implement this detector, four estimations based one the Maximum-Likelihood-Estimator (MLE) are required: model order, frequency, phase and amplitude estimations. Frequencies are estimated by Total Least Squares-Estimation of Signal Parameters via Rotational Invariance Techniques (TLS-ESPRIT). Two types of faults are considered: bearing and broken rotor bars faults. Experimental tests clearly show the effectiveness of the proposed detector