System dynamic reliability assessment and failure prognostics

Jie Liu (1) , Enrico Zio (2, 1)

1 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec
2 LGI - Laboratoire Génie Industriel - EA 2606

Traditionally, equipment reliability assessment is based on failure data from a population of similar equipment, somewhat giving an average description of the reliability performance of an equipment, not capturing the specificity of the individual equipment. Monitored degradation data of the equipment can be used to specify its behavior, rendering dynamic the reliability assessment and the failure prognostics of the equipment, as shown in some recent literature. In this paper, dynamic reliability assessment and failure prognostics with noisy monitored data are developed for a system composed of dependent components. Parallel Monte Carlo simulation and recursive Bayesian method are integrated in the proposed modelling framework to assess the reliability and to predict the Remaining Useful Life (RUL) of the system. The main contribution of the paper is to propose a framework to estimate the degradation state of a system composed of dependent degradation components whose conditions are monitored (even without knowing the initial system degradation state) and to dynamically assess the system risk and RUL. As case study, a subsystem of the residual heat removal system of a nuclear power plant is considered. The results shows the significance of the proposed method for tailored reliability assessment and failure prognostics.

Mots clés: Multiple component; Monte Carlo simulation; Failure prognostics; Dependent degradation; Recursive Bayesian method; Reliability assessment

Domaines: Génie civil; Risques

Jie Liu, Enrico Zio. System dynamic reliability assessment and failure prognostics. Reliability Engineering and System Safety, 2017, 160, pp.21 - 36. ⟨10.1016/j.ress.2016.12.003⟩. ⟨hal-01652269⟩