Unsupervised clustering of vibration signals for identifying anomalous conditions in a nuclear turbine

Piero Baraldi (1) , Francesco Di Maio (1) , Marco Rigamonti (1) , Enrico Zio (2) , Redouane Seraoui (3)

1 Dipartimento di Energia [Milano]
2 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec
3 EDF R&D - EDF R&D

We consider a real industrial case concerning 148 shutdown multidimensional transients of a nuclear power plant (NPP) turbine. The objective is to identify groups of transients with similar functional behaviors, and distinguish transients with peculiar behaviors which can be representative of anomalous conditions in the turbine. This objective is pursued by analyzing 7 vibration signals referred to the turbine shaft. The novelty of the work consists in transforming the signals into the " turbine speed-domain " for aligning them according to the turbine speed, so as to easily recognize outlier transients and then performing a fuzzy similarity analysis based on pointwise differences. Spectral analysis and Fuzzy C-Means (FMC) clustering are applied to identify the turbine anomalous conditions.

Domaines: Energie électrique

Piero Baraldi, Francesco Di Maio, Marco Rigamonti, Enrico Zio, Redouane Seraoui. Unsupervised clustering of vibration signals for identifying anomalous conditions in a nuclear turbine. Journal of Intelligent and Fuzzy Systems, 2015, 28 (4), pp.1723-1731. ⟨10.3233/ifs-141459⟩. ⟨hal-01265647⟩