A Reliability Assessment Framework for Systems With Degradation Dependency by Combining Binary Decision Diagrams and Monte Carlo Simulation

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 46; no. 11; pp. 1556 - 1564
• Main Authors: Yan-Hui Lin, Yan-Fu Li, Zio, Enrico
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Binary decision diagrams (BDDs); Boolean functions; Computational modeling; Data structures; Degradation; degradation dependency; Engineering Sciences; Markov analysis; Mathematical model; Monte Carlo simulation; Monte Carlo simulation (MCS); Nuclear power plants; Physics; piecewise-deterministic Markov process (PDMP); Reliability; system reliability analysis
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2015.2500020

Components are often subject to multiple competing degradation processes. This paper presents a reliability assessment framework for multicomponent systems whose component degradation processes are modeled by multistate and physics-based models with limited statistical degradation/failure data. The piecewise-deterministic Markov process modeling approach is employed to treat dependencies between the degradation processes within one component or/and among components. A computational method combining binary decision diagrams (BDDs) and Monte Carlo simulation (MCS) is developed to solve the model. A BDD is used to encode the fault tree of the system and obtain all the paths leading to system failure or operation. MCS is used to generate random realizations of the model and compute the system reliability. A case study is presented, with reference to one branch of the residual heat removal system of a nuclear power plant.