Dynamic Model-based Saddle-point Approximation for Reliability and Reliability-based Sensitivity Analysis. (September 2020)

• Record Type: Journal Article
• Authors: Zhou, Di; Pan, Ershun; Zhang, Xufang; Zhang, Yimin
• Is Part Of: Reliability engineering & system safety. Volume 201(2020)
• Publication Date: 2020-09
• Subjects: Dynamic reliability-based sensitivity -- Dynamic reliability -- Saddle-point approximation -- Load-strength interference model; 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.2020.106972
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Dynamic probability distribution is established by saddle-point approximate method. Approximate dynamic reliability is established and model accuracy is investigated. Dynamic reliability-based sensitivity is investigated for the parameters' effects. The fixed-threshold model and load-strength interference model are investigated. Abstract: Dynamic reliability analysis must consider both dynamic performance and parametric randomness to evaluate system safety in operation. The saddle-point approximation method is utilized to establish the dynamic probability distribution for known or unknown types of random variables. The cumulative distribution function is used to establish the approximation dynamic reliability model based on a simplified formula with high accuracy. Dynamic reliability analysis is investigated to describe the system safety of the moving operation process. Additionally, dynamic reliability-based sensitivity is used to represent the influence of the parameters on the system's dynamic performance. The fixed-threshold model and load-strength interference model are considered to develop computational formulas of reliability analysis and the degree of effect of each parameter's fluctuation. Finally, the proposed method is evaluated and demonstrated by means of four numerical examples to analyse the system performance and the parameters' effects. The crude Monte Carlo simulation is performed to provide benchmark results.