TY - JOUR

T1 - Reliability analysis of a complex system with hybrid structures and multi-level dependent life metrics

AU - Yang, Lechang

AU - Wang, Pidong

AU - Wang, Qiang

AU - Bi, Sifeng

AU - Peng, Rui

AU - Behrensdorf, Jasper

AU - Beer, Michael

N1 - Funding Information: This work is partially supported by the National Natural Science Foundation of China under Grant 52005032 , 72071005 , the Aeronautical Science Foundation of China under Grant 2018ZC74001 , the Fundamental Research Funds for the Central Universities of China under Grant FRF-TP-20-008A2 and the China Scholarship Council (CSC) under Grant 201906465064 .

PY - 2021/5

Y1 - 2021/5

N2 - In practical engineering, the presence of dependent evidence is not rare due to various imperfections. Misuse of such information in reliability analysis will lead to conflicting or even erroneous results. In this paper, we propose a Bayesian reliability approach for complex systems with dependent life metrics. Notions such as explicit evidence and implicit evidence are established based on an identification of different roles of multiple dependent evidence in the likelihood construction. A likelihood decomposition method is developed to convert the overall likelihood into a product of Explicit Evidence-based Likelihood (EEL) function and Implicit Evidence-based Likelihood (IEL) function. An inferential diagram is developed to intuitively generate the required implicit evidence taking both outer-source information and the system configuration into consideration. An algorithm is then presented for implementation. The contribution of our work is a systematic investigation of the role of dependent evidence in system reliability evaluation and a full Bayesian approach that is applied to various system reliability models. Extensive numerical cases and a practical engineering case are demonstrated for validation and to illustrate the benefits of our approach.

AB - In practical engineering, the presence of dependent evidence is not rare due to various imperfections. Misuse of such information in reliability analysis will lead to conflicting or even erroneous results. In this paper, we propose a Bayesian reliability approach for complex systems with dependent life metrics. Notions such as explicit evidence and implicit evidence are established based on an identification of different roles of multiple dependent evidence in the likelihood construction. A likelihood decomposition method is developed to convert the overall likelihood into a product of Explicit Evidence-based Likelihood (EEL) function and Implicit Evidence-based Likelihood (IEL) function. An inferential diagram is developed to intuitively generate the required implicit evidence taking both outer-source information and the system configuration into consideration. An algorithm is then presented for implementation. The contribution of our work is a systematic investigation of the role of dependent evidence in system reliability evaluation and a full Bayesian approach that is applied to various system reliability models. Extensive numerical cases and a practical engineering case are demonstrated for validation and to illustrate the benefits of our approach.

KW - Bayesian network

KW - Dependent life metrics

KW - Hybrid structure

KW - Likelihood function

KW - Multi-source conflicting information

KW - Reliability analysis

UR - http://www.scopus.com/inward/record.url?scp=85099615059&partnerID=8YFLogxK

U2 - 10.1016/j.ress.2021.107469

DO - 10.1016/j.ress.2021.107469

M3 - Article

AN - SCOPUS:85099615059

VL - 209

JO - Reliability engineering & system safety

JF - Reliability engineering & system safety

SN - 0951-8320

M1 - 107469

ER -