Dynamic reliability assessment and prediction for repairable systems with interval-censored data

• Published in: Reliability engineering & system safety Vol. 159; pp. 301 - 309
• Main Authors: Peng, Yizhen, Wang, Yu, Zi, YanYang, Tsui, Kwok-Leung, Zhang, Chuhua
• Format: Journal Article
• Language: English
• Published: Barking Elsevier Ltd 01.03.2017; Elsevier BV
• Subjects: Computer simulation; Data processing; Dependent variables; Dynamic reliability; Independent variables; Inspection; Interval censoring; Malfunctions; Mathematical models; Missing data; Monte carlo expectation-maximization algorithm; Monte Carlo simulation; Natural gas; Non-homogeneous Poisson process; Parameter estimation; Poisson density functions; Reliability; Reliability analysis; Reliability engineering; System reliability; Dynamic reliability; Interval censoring; Monte carlo expectation-maximization algorithm; Non-homogeneous Poisson process
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2016.11.011

The ‘Test, Analyze and Fix’ process is widely applied to improve the reliability of a repairable system. In this process, dynamic reliability assessment for the system has been paid a great deal of attention. Due to instrument malfunctions, staff omissions and imperfect inspection strategies, field reliability data are often subject to interval censoring, making dynamic reliability assessment become a difficult task. Most traditional methods assume this kind of data as multiple normal distributed variables or the missing mechanism as missing at random, which may cause a large bias in parameter estimation. This paper proposes a novel method to evaluate and predict the dynamic reliability of a repairable system subject to interval-censored problem. First, a multiple imputation strategy based on the assumption that the reliability growth trend follows a nonhomogeneous Poisson process is developed to derive the distributions of missing data. Second, a new order statistic model that can transfer the dependent variables into independent variables is developed to simplify the imputation procedure. The unknown parameters of the model are iteratively inferred by the Monte Carlo expectation maximization (MCEM) algorithm. Finally, to verify the effectiveness of the proposed method, a simulation and a real case study for gas pipeline compressor system are implemented. •A new multiple imputation strategy was developed to derive the PDF of missing data.•A new order statistic model was developed to simplify the imputation procedure.•The parameters of the order statistic model were iteratively inferred by MCEM.•A real cases study was conducted to verify the effectiveness of the proposed method.