Genetic algorithm-based wrapper approach for grouping condition monitoring signals of nuclear power plant components

  • Authors:

  • Piero Baraldi;Roberto Canesi;Enrico Zio;Redouane Seraoui;Roger Chevalier

  • Affiliations:

  • (CorrespD. Tel.: +39 02 2399 6355/ Fax: +39 02 2399 6309/ E-mail piero.baraldi@polimi.it) Dipartimento di Energia, Politecnico di Milan, Milan, Italy;Dipartimento di Energia, Politecnico di Milan, Milan, Italy;Dipartimento di Energia, Politecnico di Milan, Milan, Italy AND Ecole Centrale Paris - Supelec, Paris, France;Simulation and information Technologies for Power Generation Systems Department, EDF R&D, Chatou Cedex, France;Simulation and information Technologies for Power Generation Systems Department, EDF R&D, Chatou Cedex, France

  • Venue:
  • Integrated Computer-Aided Engineering - Data Mining in Engineering
  • Year:
  • 2011

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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.