Application of Bayesian Networks in Reliability Evaluation

被引:196
作者
Cai, Baoping [1 ]
Kong, Xiangdi [2 ]
Liu, Yonghong [2 ]
Lin, Jing [3 ]
Yuan, Xiaobing [2 ]
Xu, Hongqi [4 ]
Ji, Renjie [2 ]
机构
[1] China Univ Petr, Coll Mech & Elect Engn, Qingdao 266580, Shandong, Peoples R China
[2] China Univ Petr, Qingdao 266580, Shandong, Peoples R China
[3] Lulea Univ Technol, Lulea, Sweden
[4] Rongsheng Machinery Manufacture Ltd Huabei Oil Fi, Renqiu 062550, Hebei, Peoples R China
基金
中国国家自然科学基金;
关键词
Bayesian network (BN); hardware; human; reliability; software; structure; SUBSEA BLOWOUT PREVENTER; BELIEF NETWORKS; EVALUATION METHODOLOGY; SITUATION ASSESSMENT; GAME APPROACH; FAULT-TREES; SYSTEMS; SOFTWARE; MODEL; FUZZY;
D O I
10.1109/TII.2018.2858281
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Bayesian network (BN) is a powerful model for probabilistic knowledge representation and inference and is increasingly used in the field of reliability evaluation. This paper presents a bibliographic review of BNs that have been proposed for reliability evaluation in the last decades. Studies are classified from the perspective of the objects of reliability evaluation, i.e., hardware, structures, software, and humans. For each classification, the construction and validation of a BN-based reliability model are emphasized. The general procedural steps for BN-based reliability evaluation, including BN structure modeling, BN parameter modeling, BN inference, and model verification and validation, are investigated. Current gaps and challenges in reliability evaluation with BNs are explored, and a few upcoming research directions that are of interest to reliability researchers are identified.
引用
收藏
页码:2146 / 2157
页数:12
相关论文
共 102 条
[1]   Human reliability assessment (HRA) in maintenance of production process: a case study [J].
Aalipour M. ;
Ayele Y.Z. ;
Barabadi A. .
International Journal of System Assurance Engineering and Management, 2016, 7 (2) :229-238
[2]  
[Anonymous], 2010, IEC 61508-1:2010
[3]   Bayesian network based software reliability prediction with an operational profile [J].
Bai, CG .
JOURNAL OF SYSTEMS AND SOFTWARE, 2005, 77 (02) :103-112
[4]   Software failure prediction based on a Markov Bayesian network model [J].
Bai, CG ;
Hu, QP ;
Xie, M ;
Ng, SH .
JOURNAL OF SYSTEMS AND SOFTWARE, 2005, 74 (03) :275-282
[5]   Comparing the treatment of uncertainty in Bayesian networks and fuzzy expert systems used for a human reliability analysis application [J].
Baraldi, Piero ;
Podofillini, Luca ;
Mkrtchyan, Lusine ;
Zio, Enrico ;
Dang, Vinh N. .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2015, 138 :176-193
[6]   Improving the analysis of dependable systems by mapping fault trees into Bayesian networks [J].
Bobbio, A ;
Portinale, L ;
Minichino, M ;
Ciancamerla, E .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2001, 71 (03) :249-260
[7]   A discrete-time Bayesian network reliability modeling and analysis framework [J].
Boudali, H ;
Dugan, JB .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2005, 87 (03) :337-349
[8]  
Bradbury-Squires D. J., 2013, SIMULATION TRAINING
[9]   Availability-based engineering resilience metric and its corresponding evaluation methodology [J].
Cai, Baoping ;
Xie, Min ;
Liu, Yonghong ;
Liu, Yiliu ;
Feng, Qiang .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2018, 172 :216-224
[10]   Bayesian Networks in Fault Diagnosis [J].
Cai, Baoping ;
Huang, Lei ;
Xie, Min .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2017, 13 (05) :2227-2240