Risk assessment methods are often used in complex industrial systems to avoid risks and reduce losses. The existing methods have not effectively solved the problems of lack of evaluation data and the interpretability of the entire evaluation process. This paper proposes a new risk assessment model based on the belief rule base (BRB) and Fault Tree Analysis (FTA). The FTA algorithm overcomes the difficulties of traditional BRB model in obtaining expert knowledge, clear indicators, and establishing logical relationships. This method establishes FTA rules based on the BRB model and expands the knowledge base through the FTA algorithm. A Bayesian network is applied as a conversion bridge between the FTA and BRB model. In addition, the model is optimized to reduce the uncertainty in the model. The method proposed is described by a case and its effectiveness is verified.
DengSYueDFuX, et al. Security risk assessment of cyber physical power system based on rough set and gene expression programming. IEEE/CAA J Automatica Sinica2015; 4(2): 431–439.
2.
WuCWenTZhangY.A revised CVSS-based system to improve the dispersion of vulnerability risk scores. Sci China Inf Sci2019; 62(3): 39102.
3.
HuangHWangJFeiC, et al. A probabilistic risk assessment framework considering lane-changing behavior interaction. Sci China Inf Sci2020; 63(9): 190203.
4.
LiuCYangSWCuiY, et al. An improved risk assessment method based on a comprehensive weighting algorithm in railway signaling safety analysis. Saf Sci2020; 128: 104768.
5.
CherdantsevaYBurnapPBlythA, et al. A review of cyber security risk assessment methods for SCADA systems. Comput Secur2016; 56: 1–27.
6.
AbdollahzadehGRastgooS.Risk assessment in bridge construction projects using fault tree and event tree analysis methods based on fuzzy logic. ASCE-ASME J Risk Uncertain Eng Syst B2015; 1(3): 031006.
7.
Bani-MustafaTFlageRVasseurD, et al. An extended method for evaluating assumptions deviations in quantitative risk assessment and its application to external flooding risk assessment of a nuclear power plant. Reliab Eng Syst Saf2020; 200: 106947.
8.
ZhangTZhangWXuW, et al. Multiple instance learning for credit risk assessment with transaction data. Knowl Based Syst2018; 161: 65–77.
9.
TianDYangBChenJ, et al. A multi-experts and multi-criteria risk assessment model for safety risks in oil and gas industry integrating risk attitudes. Knowl Based Syst2018; 156: 62–73.
10.
TianSF. Base on the analysis of key factors of the real estate investment risk prevention strategies. In: 2015 seventh international conference on measuring technology and mechatronics automation, Nanchang, China, 2015, pp.221–223. IEEE.
11.
BjornsenKJensenAAvenT, et al. Using qualitative types of risk assessments in conjunction with FRAM to strengthen the resilience of systems. J Risk Res2020; 23(2): 153–166.
12.
OppligerR.Quantitative risk analysis in information security management: a modern fairy tale. IEEE Secur Priv2015; 13(6): 18–21.
13.
LangdalenHAbrahamsenEBAbrahamsenHB, et al. A new framework to idenitfy and assess hidden assumptions in the background knowledge of a risk assessment. Reliab Eng Syst Saf2020; 200: 106909.
14.
KongGXuDLBodyR, et al. A belief rule-based decision support system for clinical risk assessment of cardiac chest pain. Eur J Oper Res2012; 219(3): 564–573.
15.
YangLHWangYMChangLL, et al. A disjunctive belief rule-based expert system for bridge risk assessment with dynamic parameter optimization model. Comput Ind Eng2017; 113: 459–474.
16.
QiuSQSallakMSchonW, et al. A valuation-based system approach for risk assessment of belief rule-based expert systems. Inf Sci2018; 466: 323–336.
17.
GachlouMRoozbahaniABanihabibME, et al. Comprehensive risk assessment of river basins using fault tree analysis. J Hydrol2019; 577: 123974.
18.
HyunKCMinSChoiH, et al. Risk analysis using fault-tree analysis (FTA) and analytic hierarchy process (AHP) applicable to shield TBM tunnels. Tunn Undergr Space Technol2015; 49: 121–129.
19.
LiGZhouZHuC, et al. A new safety assessment model for complex system based on the conditional generalized minimum variance and the belief rule base. Saf Sci2017; 93: 108–120.
20.
FengZZhouZHuC, et al. A safety assessment model based on belief rule base with new optimization method. Reliab Eng Syst Saf2020; 203: 107055.
21.
YangJBLiuJWangJ, et al. Belief rule-base inference methodology using the evidential reasoning approach—RIMER. IEEE Trans Syst Man Cybern A2006; 36(2): 266–285.
22.
ZhouZFengZCHuCH, et al. Aeronautical relay health state assessment model based on belief rule base with attribute reliability. Knowl Based Syst2020; 197: 105869.
23.
ZhouZJHuGYChenCL, et al. A survey of belief rule-base expert system. IEEE Trans Syst Man Cybern2019; 11: 1–15.
24.
ZhouZJHuGYZhangBC, et al. A model for hidden behavior prediction of complex systems based on belief rule base and power set. IEEE Trans Syst Man Cybern2018; 48(9): 1649–1655.
25.
FengZCZhouZ-JHuC, et al. A new belief rule base model with attribute reliability. IEEE Trans Fuzzy Syst2019; 27(5): 903–916.
26.
ZhongKHanMHanB.Data-driven based fault prognosis for industrial systems: a concise overview. IEEE/CAA J Automatica Sinica2020; 7(2): 330–345.
27.
GuoXSuiSWangB, et al. A current-based approach for short-circuit fault diagnosis in closed-loop current source inverter. IEEE Trans Ind Electron2020; 67(9): 7941–7950.
28.
WangQ. Subway crowded and stampede neural networks safety assessment basing on SHEL model. In: LongSDhillonB (eds.) Man-machine-environment system engineering. MMESE 2016. Lecture notes in electrical engineering. vol. 406. Singapore: Springer, 2016, pp.455–468.
29.
YazdiMKabirSWalkerM.Uncertainty handling in fault tree based risk assessment: state of the art and future perspectives. Process Saf Environ Prot2019; 131: 89–104.
30.
KabirSTanKGKumarM, et al. A method for temporal fault tree analysis using intuitionistic fuzzy set and expert elicitation. IEEE Access2020; 8: 980–996.
31.
YazdiMKabirS.Fuzzy evidence theory and Bayesian networks for process systems risk analysis. Hum Ecol Risk Assess2020; 26(1): 57–86.
32.
KabirSMohammadYIgnacioAJ, et al. Uncertainty-aware dynamic reliability analysis framework for complex systems. IEEE Access2018; 6: 29499–29515.
33.
YangJBXuDL. A study on generalising Bayesian inference to evidential reasoning. In: CuzzolinF (eds) Belief functions: theory and applications. BELIEF 2014. Oxford, UK: Springer, 2014, 8764: pp.180–189.
