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Authors: Baraldi, Piero | Canesi, Roberto | Zio, Enrico | Seraoui, Redouane | Chevalier, Roger

Article Type: Research Article

Abstract: Equipment condition monitoring of nuclear power plants requires to optimally group the usually very large number of signals and to develop for each identified group a separate condition monitoring model. In this paper we propose an approach to optimally group the signals. We use a Genetic Algorithm (GA) for the optimization of the groups; the decision variables of the optimization problem relate to the composition of the groups (i.e., which signals they contain) and the objective function (fitness) driving the search for the optimal grouping is constructed in terms of quantitative indicators of the performances of the condition monitoring models …themselves: in this sense, the GA search engine is a wrapper around the condition monitoring models. A real case study is considered, concerning the condition monitoring of the Reactor Coolant Pump (RCP) of a Pressurized Water Reactor (PWR). The optimization results are evaluated with respect to the accuracy and robustness of the monitored signals estimates. The condition monitoring models built on the groups found by the proposed approach outperform the model which uses all available signals, whereas they perform similarly to the models built on groups based on signal correlation. However, these latter do not guarantee the robustness of the reconstruction in case of abnormal conditions and require to a priori fix characteristics of the groups, such as the desired minimum correlation value in a group. Show more

DOI: 10.3233/ICA-2011-0375

Citation: Integrated Computer-Aided Engineering, vol. 18, no. 3, pp. 221-234, 2011

Authors: Baraldi, Piero | Di Maio, Francesco | Rigamonti, Marco | Zio, Enrico | Seraoui, Redouane

Article Type: Research Article

Abstract: We consider a real industrial case concerning 148 shut-down 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. Show more

Keywords: Fault diagnosis, spectral clustering, Fuzzy C-Means, vibration signals

DOI: 10.3233/IFS-141459

Citation: Journal of Intelligent & Fuzzy Systems, vol. 28, no. 4, pp. 1723-1731, 2015