RESEARCH PAPER
Imprecise sensitivity analysis of system reliability based on the Bayesian network and probability box
1
School of Automation Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, 611731, P.R. China
2
School of Automation Engineering, Center for System Reliability and Safety, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, 611731, P.R. China
Publication date: 2020-09-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2020;22(3):508-519
KEYWORDS
ABSTRACT
Sensitivity analysis measures how changes in system inputs affect outputs. Previously, a large amount of sensitivity analysis research was relevant to the precise probability that is regarded as an ideal condition of engineering. Due to insufficient test samples
and the low accuracy of test data, system reliability with hybrid uncertainty is difficult to be described as a precise value. As a
profusion of highly integrated electromechanical equipment is applied in modern life, it is impossible to apply sufficient resources
to eliminate the stochastic property of every component, which necessitates the identification of highly sensitive components to
efficiently reduce imprecision. Hence, based on the theory of imprecise probability, imprecise sensitivity analysis has become a
popular research topic in the last decade. In this paper, a method for uncertain system reliability and imprecise sensitivity analysis
is proposed based on a Bayesian network, a probability box and the pinching method. The feasibility and accuracy of the combined
method are fully verified through the evaluation and analysis of a numerical example and a case study of an electromechanical
system, and the highly sensitive components that heavily influence the imprecision of system outputs are accurately identified.
REFERENCES (31)
1.
Guo H, Yang X. A simple reliability block diagram method for safety integrity verification. Reliability Engineering & System Safety 2007; 92(9): 1267-1273, https://doi.org/10.1016/j.ress....
2.
Simon C, Weber P, Evsukoff A. Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis. Reliability Engineering & System Safety 2008; 93(7): 950-963, https://doi.org/10.1016/j.ress....
3.
Simon C, Weber P, Levrat E. Bayesian Networks and Evidence Theory to Model Complex Systems Reliability. JCP 2007; 2(1): 33-43, https://doi.org/10.4304/jcp.2.....
4.
Ericson C A. Fault tree analysis. System Safety Conference, Orlando, Florida, 1999; 1: 1-9.
5.
Pearl J. Fusion, propagation, and structuring in belief networks. Artificial Intelligence 1986; 29(3): 241-288, https://doi.org/10.1016/0004-3....
6.
Cai B, Liu Y, Liu Z, Tian X, Dong X, Yu S. Using Bayesian networks in reliability evaluation for subsea blowout preventer control system. Reliability Engineering & System Safety 2012; 108: 32-41, https://doi.org/10.1016/j.ress....
7.
Su C, Fu Y. Reliability assessment for wind turbines considering the influence of wind speed using bayesian network. Eksploatacja i Niezawodnosc -Maintenance and Reliability 2014; 16(1): 1-8.
8.
Mi J, Li YF, Beer M, Broggi M, Cheng Y. Importance measure of probabilistic common cause failures under system hybrid uncertainty based on bayesian network. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(1): 112-120, https://doi.org/10.17531/ein.2....
9.
Bi S, Broggi M, Wei P, Beer M. The Bhattacharyya distance: Enriching the p-box in stochastic sensitivity analysis. Mechanical Systems and Signal Processing 2019; 129: 265-281, https://doi.org/10.1016/j.ymss....
10.
Mi J, Li YF, Yang YJ, Peng W, Huang HZ. Reliability assessment of complex electromechanical systems under epistemic uncertainty. Reliability Engineering & System Safety 2016; 152: 1-15, https://doi.org/10.1016/j.ress....
11.
Xiahou T, Liu Y. Reliability bounds for multi-state systems by fusing multiple sources of imprecise information. IISE Transactions 2019; 0(0): 1-18, https://doi.org/10.1080/247258....
12.
Dempster A P. The Dempster-Shafer calculus for statisticians. International Journal of approximate reasoning 2008; 48(2): 365-377, https://doi.org/10.1016/j.ijar....
13.
Misuri A, Khakzad N, Reniers G, Cozzani V. Tackling uncertainty in security assessment of critical infrastructures: Dempster-Shafer Theory vs. Credal Sets Theory. Safety Science 2018; 107: 62-76, https://doi.org/10.1016/j.ssci....
14.
Aguirre F, Sallak M, Schön W. Construction of Belief Functions From Statistical Data About Reliability Under Epistemic Uncertainty. IEEE Transactions on Reliability 2013; 62(3): 555-568, https://doi.org/10.1109/TR.201....
15.
Yang J, Huang HZ, He LP, Wen D, Zhu S-P. Failure Mode and Effects Analysis of Compressor Blades of Aeroengines Using Dempster-Shafer Evidence Theory. American Society of Mechanical Engineers Digital Collection: 2012: 863-870.
16.
Mi J, Li YF, Peng W, Huang H-Z. Reliability analysis of complex multi-state system with common cause failure based on evidential networks. Reliability Engineering & System Safety 2018; 174: 71-81, https://doi.org/10.1016/j.ress....
17.
Feng G, Patelli E, Beer M, Coolen F P A. Imprecise system reliability and component importance based on survival signature. Reliability Engineering & System Safety 2016; 150: 116-125, https://doi.org/10.1016/j.ress....
18.
Schöbi R, Sudret B. Global sensitivity analysis in the context of imprecise probabilities (p-boxes) using sparse polynomial chaos expansions. Reliability Engineering & System Safety 2019; 187: 129-141, https://doi.org/10.1016/j.ress....
19.
Ferson S, Kreinovich V, Grinzburg L, S.Myers D. Constructing probability boxes and Dempster-Shafer structures. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States) 2015.
20.
Wei P, Lu Z, Song J. Variable importance analysis: A comprehensive review. Reliability Engineering & System Safety 2015; 142: 399-432, https://doi.org/10.1016/j.ress....
21.
Wei P, Lu Z, Ruan W, Song J. Regional sensitivity analysis using revised mean and variance ratio functions. Reliability Engineering & System Safety 2014; 121: 121-135, https://doi.org/10.1016/j.ress....
22.
Alvarez D A. Reduction of uncertainty using sensitivity analysis methods for infinite random sets of indexable type. International journal of approximate reasoning 2009; 50(5): 750-762, https://doi.org/10.1016/j.ijar....
23.
Sankararaman S, Mahadevan S. Separating the contributions of variability and parameter uncertainty in probability distributions. Reliability Engineering & System Safety 2013; 112: 187-199, https://doi.org/10.1016/j.ress....
24.
Krzykacz-Hausmann B. An approximate sensitivity analysis of results from complex computer models in the presence of epistemic and aleatory uncertainties. Reliability Engineering & System Safety 2006; 91(10): 1210-1218, https://doi.org/10.1016/j.ress....
25.
Oberguggenberger M, King J, Schmelzer B. Classical and imprecise probability methods for sensitivity analysis in engineering: A case study. International Journal of Approximate Reasoning 2009; 50(4): 680-693, https://doi.org/10.1016/j.ijar....
26.
Helton J C, Johnson J D, Oberkampf W L, Sallaberry C J. Sensitivity analysis in conjunction with evidence theory representations of epistemic uncertainty. Reliability Engineering & System Safety 2006; 91(10): 1414-1434, https://doi.org/10.1016/j.ress....
27.
Ferson S, Troy Tucker W. Sensitivity analysis using probability bounding. Reliability Engineering & System Safety 2006; 91(10): 1435-1442, https://doi.org/10.1016/j.ress....
28.
Mi J, Cheng Y, Song Y, Bai L, Chen K. Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions. Annals of Operations Research, 2019:1-23, https://doi.org/10.1007/s10479....
29.
Montgomery V. New statistical metliods in risk assessment by probability bounds 2009.
30.
Huang X, Coolen F P A. Reliability sensitivity analysis of coherent systems based on survival signature. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2018; 232(6): 627-634, https://doi.org/10.1177/174800....
31.
Feng G, George-Williams H, Patelli E, Coolen F P A, Beer M. An efficient reliability analysis on complex non-repairable systems with common-cause failures. Safety and Reliability-Safe Societies in a Changing World-Proceedings of the 28th International European Safety and Reliability Conference; 2018: 2531-2538, https://doi.org/10.1201/978135....
CITATIONS (4):
1.
An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties
Jinhua Mi, Ning Lu, Yan-Feng Li, Hong-Zhong Huang, Libing Bai
Reliability Engineering & System Safety
Jinhua Mi, Ning Lu, Yan-Feng Li, Hong-Zhong Huang, Libing Bai
Reliability Engineering & System Safety
2.
Reliability analysis of discrete multi-state systems based on survival signature
H Liang, J Mi, L Bai, Y Cheng
IOP Conference Series: Materials Science and Engineering
H Liang, J Mi, L Bai, Y Cheng
IOP Conference Series: Materials Science and Engineering
3.
Reliability Evaluation of Environmental Test Chambers Based on Bayesian Network
Huajian Yang, Wangqiang Niu, Chengjing Han, Xianwen Zhou, Wei Gu
Journal of Failure Analysis and Prevention
Huajian Yang, Wangqiang Niu, Chengjing Han, Xianwen Zhou, Wei Gu
Journal of Failure Analysis and Prevention
4.
Reliability analysis for complex electromechanical multi-state systems utilizing universal generating function techniques
Weifu Xia, Yanhui Wang, Yucheng Hao, Zhichao He, Kai Yan, Fan Zhao
Reliability Engineering & System Safety
Weifu Xia, Yanhui Wang, Yucheng Hao, Zhichao He, Kai Yan, Fan Zhao
Reliability Engineering & System Safety
Share
RELATED ARTICLE
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
