Unsupervised Clustering for Fault Diagnosis in Nuclear Power Plant Components

DOI

10.1080/18756891.2013.804145How to use a DOI?

Keywords

Fault diagnosis, unsupervised clustering, Haar wavelets, fuzzy similarity, spectral clustering, Fuzzy C-Means

Abstract

The development of empirical classification models for fault diagnosis usually requires a process of training based on a set of examples. In practice, data collected during plant operation contain signals measured in faulty conditions, but they are ‘unlabeled’, i.e., the indication of the type of fault is usually not available. Then, the objective of the present work is to develop a methodology for the identification of transients of similar characteristics, under the conjecture that faults of the same type lead to similar behavior in the measured signals. The proposed methodology is based on the combined use of Haar wavelet transform, fuzzy similarity, spectral clustering and the Fuzzy C-Means algorithm. A procedure for interpreting the fault cause originating the similar transients is proposed, based on the identification of prototypical behaviors. Its performance is tested with respect to an artificial case study and then applied on transients originated by different faults in the pressurizer of a nuclear power reactor.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Piero Baraldi
AU  - Francesco Di Maio
AU  - Enrico Zio
PY  - 2013
DA  - 2013/07/01
TI  - Unsupervised Clustering for Fault Diagnosis in Nuclear Power Plant Components
JO  - International Journal of Computational Intelligence Systems
SP  - 764
EP  - 777
VL  - 6
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.804145
DO  - 10.1080/18756891.2013.804145
ID  - Baraldi2013
ER  -