Incipient fault detection in induction machine stator-winding using a fuzzy-Bayesian change point detection approach

In this paper the incipient fault detection problem in induction machine stator-winding is considered. The problem is solved using a new technique of change point detection in time series, based on a two-step formulation. The first step consists of a fuzzy clustering to transform the initial data, with arbitrary distribution, into a new one that can be approximated by a beta distribution. The fuzzy cluster centers are determined by using a Kohonen neural network. The second step consists in using the Metropolis–Hastings algorithm for performing the change point detection in the transformed time series generated by the first step with that known distribution. The incipient faults are detected as long as they characterize change points in such transformed time series. The main contribution of the proposed approach is the enhanced resilience of the new failure detection procedure against false alarms, combined with a good sensitivity that allows the detection of rather small fault signals. Simulation and practical results are presented to illustrate the proposed methodology.