Incipient fault detection in induction machine stator-winding using a fuzzy-Bayesian two change points 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 three-step formulation. The technique can detect up to two change points in the time series. The first step consists of a Kohonen neural network classification algorithm that defines the model to be used, one change point or two change points. The second 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 the Kohonen neural network classification algorithm used in the first step. The last step consists in using the Metropolis-Hastings algorithm for performing the change point detection in the transformed time series generated by the second 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 in this paper, related to previous one in the Literature, is to detect up to two change points in the time series considered, besides the enhanced resilience of the new fault detection procedure against false alarms, combined with a good sensitivity that allows the detection of rather small fault signals. Simulation results are presented to illustrate the proposed methodology.