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Journal articles
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.
Cited literature [38 references]
https://hal-supelec.archives-ouvertes.fr/hal-00934649
Contributor : Yanfu Li <>
Submitted on : Wednesday, January 22, 2014 - 2:03:29 PM
Last modification on : Monday, December 14, 2020 - 12:28:40 PM
Long-term archiving on: : Thursday, April 24, 2014 - 11:05:29 AM
Contributor : Yanfu Li <>
Submitted on : Wednesday, January 22, 2014 - 2:03:29 PM
Last modification on : Monday, December 14, 2020 - 12:28:40 PM
Long-term archiving on: : Thursday, April 24, 2014 - 11:05:29 AM
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