By Topic

Fuzzy/Bayesian change point detection approach to incipient fault detection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
D'Angelo, M.F.S. ; Dept. of Comput. Sci., UNIMONTES, Montes Claros, Brazil ; Palhares, R.M. ; Takahashi, R.H.C. ; Loschi, R.H.

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.

Published in:

Control Theory & Applications, IET  (Volume:5 ,  Issue: 4 )