A fuzzy expectation maximization based method for estimating the parameters of a multi-state degradation model from imprecise maintenance outcomes. (December 2017)

• Record Type: Journal Article
• Authors: Cannarile, F.; Compare, M.; Rossi, E.; Zio, E.
• Is Part Of: Annals of nuclear energy. Volume 110(2017:Dec.)
• Page Start: 739
• Page End: 752
• Publication Date: 2017-12
• Subjects: Multi-State Systems -- Homogeneous Continuous-Time Semi-Markov Process (HCTSMP) -- Weibull distribution -- Fuzzy Expectation-Maximization (FEM) -- Residual Useful Life (RUL) -- Piping System (PS) -- Nuclear Power Plants (NPPs); Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2017.07.017
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: We develop a Fuzzy EM algorithm to estimate the parameters of a MS degradation model of a NPP PS. We exploit both field inspection outcomes and maintenance operators' judgments. Possibility distributions are used to describe the imprecision in the expert statements. PS RUL is estimated based on the MS model and conditional on imprecise evidence. Abstract: Multi-State (MS) reliability models are used in practice to describe the evolution of degradation in industrial components and systems. To estimate the MS model parameters, we propose a method based on the Fuzzy Expectation-Maximization (FEM) algorithm, which integrates the evidence of the field inspection outcomes with information taken from the maintenance operators about the transition times from one state to another. Possibility distributions are used to describe the imprecision in the expert statements. A procedure for estimating the Remaining Useful Life (RUL) based on the MS model and conditional on such imprecise evidence is, then, developed. The proposed method is applied to a case study concerning the degradation of pipe welds in the coolant system of a Nuclear Power Plant (NPP). The obtained results show that the combination of field data with expert knowledge can allow reducing the uncertainty in degradation estimation and RUL prediction.