Fault Detection in Nuclear Power Plants Components by a Combination of Statistical Methods

Abstract : In this paper, we investigate the feasibility of a strategy of fault detection capable of controlling misclassification probabilities, i.e., balancing false and missed alarms. The novelty of the proposed strategy consists of i) a signal grouping technique and signal reconstruction modeling technique (one model for each subgroup), and ii) a statistical method for defining the fault alarm level. We consider a real case study concerning 46 signals of the Reactor Coolant Pump (RCP) of a typical Pressurized Water Reactor (PWR). In the application, the reconstructions are provided by a set of Auto-Associative Kernel Regression (AAKR) models, whose input signals have been selected by a hybrid approach based on Correlation Analysis (CA) and Genetic Algorithm (GA) for the identification of the groups. Sequential Probability Ratio Test (SPRT) is used to define the alarm level for a given expected classification performance. A practical guideline is provided for optimally setting the SPRT parameters' values.
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IEEE Transactions on Reliability, Institute of Electrical and Electronics Engineers, 2013, 62 (4), pp.833 - 845. 〈10.1109/TR.2013.2285033〉
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Francesco Di Maio, Piero Baraldi, Enrico Zio, Rédouane Seraoui. Fault Detection in Nuclear Power Plants Components by a Combination of Statistical Methods. IEEE Transactions on Reliability, Institute of Electrical and Electronics Engineers, 2013, 62 (4), pp.833 - 845. 〈10.1109/TR.2013.2285033〉. 〈hal-00934649〉

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