Clustering for unsupervised fault diagnosis in nuclear turbine shut-down transients

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, which 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 shut-down transients of a Nuclear Power Plant (NPP) steam turbine.