An empirical classification-based framework for the safety criticality assessment of energy production systems, in presence of inconsistent data. (January 2017)

• Record Type: Journal Article
• Authors: Wang, Tai-Ran; Mousseau, Vincent; Pedroni, Nicola; Zio, Enrico
• Is Part Of: Reliability engineering & system safety. Volume 157(2017)
• Page Start: 139
• Page End: 151
• Publication Date: 2017-01
• Subjects: Safety-criticality -- Classification model -- Data consistency validation -- Confidence estimation -- MR-Sort -- Nuclear power plants; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2016.08.021
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: The technical problem addressed in the present paper is the assessment of the safety criticality of energy production systems. An empirical classification model is developed, based on the Majority Rule Sorting method, to evaluate the class of criticality of the plant/system of interest, with respect to safety. The model is built on the basis of a (limited-size) set of data representing the characteristics of a number of plants and their corresponding criticality classes, as assigned by experts. The construction of the classification model may raise two issues. First, the classification examples provided by the experts may contain contradictions: a validation of the consistency of the considered dataset is, thus, required. Second, uncertainty affects the process: a quantitative assessment of the performance of the classification model is, thus, in order, in terms of accuracy and confidence in the class assignments. In this paper, two approaches are proposed to tackle the first issue: the inconsistencies in the data examples are "resolved" by deleting or relaxing, respectively, some constraints in the model construction process. Three methods are proposed to address the second issue: (i) a model retrieval-based approach, (ii) the Bootstrap method and (iii) the cross-validation technique. Numerical analyses are presented with reference to an artificial case study regarding the classification of Nuclear Power Plants. Highlights: We use a hierarchical framework to represent safety criticality. We use an empirical classification model to evaluate safety criticality. Inconsistencies in data examples are "resolved" by deleting/relaxing constraints. Accuracy and confidence in the class assignments are computed by three methods. Method is applied to fictitious Nuclear Power Plants.