Clustering for unsupervised fault diagnosis in nuclear turbine shut-down transients
Abstract
Empirical methods for fault diagnosis usually entail a process of supervised training based on a set of examples of signal evolutions " labeled " with the corresponding, known classes of fault. However, in practice, the signals collected during plant operation may be, very often, " unlabeled " , i.e., the information on the corresponding type of occurred fault is not available. To cope with this practical situation, in this paper we develop a methodology for the identification of transient signals showing similar characteristics, under the conjecture that operational/faulty transient conditions of the same type lead to similar behavior in the measured signals evolution. The methodology is founded on a feature extraction procedure, which feeds a spectral clustering technique, embedding the unsupervised Fuzzy C-Means (FCM) algorithm, that evaluates the functional similarity among the different operational/faulty transients. A procedure for validating the plausibility of the obtained clusters is also propounded based on physical considerations. The methodology is applied to a real industrial case, on the basis of 148 shutdown transients of a Nuclear Power Plant (NPP) steam turbine.
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