Fuzzy/Bayesian change point detection approach to incipient fault detection

This study presents a novel approach for incipient fault detection in dynamical systems which is based on a two-step fuzzy/Bayesian formulation for change point detection in time series. The first step consists of a fuzzy-based clusterization to transform the initial data, with arbitrary distribution, into a new one that can be approximated with a beta distribution. The second step consists in using the Metropolis-Hastings algorithm to the change point detection in the transformed time series. The incipient fault is detected as long as it characterises a change point in such transformed time series. The problem of incipient fault detection in the RTN DAMADICS is analysed.