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 clusterisation 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.

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