Abstract
This paper proposes a novel framework for assessing the system safety of complex electromechanical systems (CEMSs). From the perspective of system topology, the fault pervasion probability (FPP) is first proposed to analyze fault propagation mechanisms in combination with historical failure data. This approach can easily identify all failure propagation paths and rapidly locate fault nodes, thereby providing a valuable reference for maintenance engineers. To overcome the influence of subjective factors, the hesitant interval-valued intuitionistic fuzzy element (HIVIFE) is used to describe the failure consequences of components and fault paths. Then, a system safety indicator is proposed to measure the system state and provide support to managers and operators through integration of the failure consequences based on the hesitant interval-valued intuitionistic fuzzy Choquet integral (HIVIFCI). The bogie system of a high-speed train is selected as a case study to verify the effectiveness and applicability of the proposed approach. The results indicate that the proposed approach can (i) achieve a more accurate result for system safety assessment and (ii) identify all possible fault propagation paths. This study provides a basis for formulating maintenance strategies and reducing accident losses, which have important theoretical value and practical significance.
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Jiang, H., Wang, R., Gao, J., Gao, Z., Gao, X.: Evidence fusion-based framework for condition evaluation of complex electromechanical system in process industry. Knowl. Based Syst. 124, 176–187 (2017)
Mcharek, M., Hammadi, M., Azib, T., Larouci, C., Choley, J.Y.: Collaborative design process and product knowledge methodology for mechatronic systems. Comput. Ind. 105, 213–228 (2019)
Wang, R., Gao, J., Gao, Z., Gao, X., Jiang, H.: Complex network theory-based condition recognition of electromechanical system in process industry. Sci. China Technol. Sci. 59(4), 604–617 (2016)
Calle, E., Ripoll, J., Segovia, J., Vilà, P., Manzano, M.: A multiple failure propagation model in GMPLS-based networks. IEEE Netw. 24(6), 17–22 (2010)
Givoni, M.: Development and impact of the modern high-speed train: a review. Transport. Rev. 26(5), 593–611 (2006)
Rao, K.D., Gopika, V., Rao, V.S., Kushwaha, H.S., Verma, A.K., Srividya, A.: Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment. Reliab. Eng. Syst. Safe. 94(4), 872–883 (2009)
Alessandri, S., Caputo, A.C., Corritore, D., Giannini, R., Paolacci, F., Phan, H.N.: Probabilistic risk analysis of process plants under seismic loading based on Monte Carlo simulations. J. Loss Prev. Process Ind. 53, 136–148 (2018)
Ferdous, R., Khan, F., Sadiq, R., Amyotte, P., Veitch, B.: Fault and event tree analyses for process systems risk analysis: uncertainty handling formulations. Risk Anal. Int. J. 31(1), 86–107 (2011)
Mechri, W., Simon, C., Bicking, F., Othman, K.B.: Fuzzy multiphase Markov chains to handle uncertainties in safety systems performance assessment. J. Loss Prev. Process Ind. 26(4), 594–604 (2013)
Giardina, M., Morale, M.: Safety study of an LNG regasification plant using an FMECA and HAZOP integrated methodology. J. Loss Prev. Process Ind. 35, 35–45 (2015)
Smith, D., Veitch, B., Khan, F., Taylor, R.: Understanding industrial safety: comparing Fault tree, Bayesian network, and FRAM approaches. J. Loss Prev. Process Ind. 45, 88–101 (2017)
Zarei, E., Khakzad, N., Cozzani, V., Reniers, G.: Safety analysis of process systems using Fuzzy Bayesian Network (FBN). J. Loss Prev. Process Ind. 57, 7–16 (2019)
Tang, K.H.D., Dawal, S.Z.M., Olugu, E.U.: Integrating fuzzy expert system and scoring system for safety performance evaluation of offshore oil and gas platforms in Malaysia. J. Loss Prev. Process Ind. 56, 32–45 (2018)
Sharvia, S., Papadopoulos, Y.: Integrating model checking with HiP-HOPS in model-based safety analysis. Reliab. Eng. Syst. Safe. 135, 64–80 (2015)
Cai, Z., Hu, J., Zhang, L., Ma, X.: Hierarchical fault propagation and control modeling for the resilience analysis of process system. Chem. Eng. Res. Des. 103, 50–60 (2015)
Hu, J., Zhang, L., Cai, Z., Wang, Y., Wang, A.: Fault propagation behavior study and root cause reasoning with dynamic Bayesian network based framework. Process Saf. Environ. 97, 25–36 (2015)
Yang, F., Xiao, D., Shah, S.L.: Signed directed graph-based hierarchical modelling and fault propagation analysis for large-scale systems. IET Control. Theory A. 7(4), 537–550 (2013)
Luo, Y., van den Brand, M.: Metrics design for safety assessment. Inform. Softw. Tech. 73, 151–163 (2016)
Li, G., Zhou, Z., Hu, C., Chang, L., Zhou, Z., Zhao, F.: A new safety assessment model for complex system based on the conditional generalized minimum variance and the belief rule base. Safety Sci. 93, 108–120 (2017)
Li, Y., Cui, L., Lin, C.: Modeling and analysis for multi-state systems with discrete-time Markov regime-switching. Reliab. Eng. Syst. Safe. 166, 41–49 (2017)
Kabir, S., Walker, M., Papadopoulos, Y.: Dynamic system safety analysis in HiP-HOPS with Petri nets and Bayesian networks. Saf. Sci. 105, 55–70 (2018)
Liu, X., An, S.: Failure propagation analysis of aircraft engine systems based on complex network. Procedia Eng. 80, 506–521 (2014)
Lu, X., Liu, M.: Hazard rate function in dynamic environment. Reliab. Eng. Syst. Safe. 130, 50–60 (2014)
Purba, J.H.: A fuzzy-based reliability approach to evaluate basic events of fault tree analysis for nuclear power plant probabilistic safety assessment. Ann. Nucl. Energy 70, 21–29 (2014)
Zio, E.: Challenges in the vulnerability and risk analysis of critical infrastructures. Reliab. Eng. Syst. Safe. 152, 137–150 (2016)
Aminbakhsh, S., Gunduz, M., Sonmez, R.: Safety risk assessment using analytic hierarchy process (AHP) during planning and budgeting of construction projects. J Safety Res. 46, 99–105 (2013)
Zhao, F.J., Zhou, Z.J., Hu, C.H., Chang, L.L., Zhou, Z.G., Li, G.L.: A new evidential reasoning-based method for online safety assessment of complex systems. IEEE Trans. Syst. Man Cy. S. 48(6), 954–966 (2018)
Bao, Q., Ruan, D., Shen, Y., Hermans, E., Janssens, D.: Improved hierarchical fuzzy TOPSIS for road safety performance evaluation. Knowl. Based Syst. 32, 84–90 (2012)
Chen, T., Jin, Y., Qiu, X., Chen, X.: A hybrid fuzzy evaluation method for safety assessment of food-waste feed based on entropy and the analytic hierarchy process methods. Expert Syst. 41, 7328–7337 (2014)
Su, B., Xie, N.: Research on safety evaluation of civil aircraft based on the grey clustering model. Grey Syst. Theory Appl. 8(1), 110–120 (2018)
Lin, S., Wang, Y., Jia, L., Zhang, H.: Reliability assessment of complex electromechanical systems: a network perspective. Qual. Reliab. Eng. Int. 34(5), 772–790 (2018)
Lin, S., Jia, L.M., Wang, Y.H., Li, Y.: Component importance measure computation method based fuzzy integral with its application. Discrete Dyn. Nat. Soc. 7842596, 18 (2017)
Zhang, Z.: Interval-valued intuitionistic hesitant fuzzy aggregation operators and their application in group decision-making. J. Appl. Math. 2013, 33 (2013)
Farhadinia, B.: Information measures for hesitant fuzzy sets and interval-valued hesitant fuzzy sets. Inf. Sci. 240, 129–144 (2013)
Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. 25(6), 529–539 (2010)
Joshi, D., Kumar, S.: Interval-valued intuitionistic hesitant fuzzy Choquet integral based TOPSIS method for multi-criteria group decision making. Eur. J. Oper. Res. 248(1), 183–191 (2016)
Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 52(3), 1059–1069 (2010)
Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: generalizing degree and shortest paths. Soc. Netw. 32(3), 245–251 (2010)
Certa, A., Hopps, F., Inghilleri, R., La Fata, C.M.: A Dempster-Shafer Theory-based approach to the Failure Mode, Effects and Criticality Analysis (FMECA) under epistemic uncertainty: application to the propulsion system of a fishing vessel. Reliab. Eng. Syst. Safe. 159, 69–79 (2017)
Zhai, Y., Xu, Z., Liao, H.: Measures of probabilistic interval-valued intuitionistic hesitant fuzzy sets and the application in reducing excessive medical examinations. IEEE T. Fuzzy Syst. 26(3), 1651–1670 (2018)
Meng, F., Tang, J.: Interval-valued intuitionistic fuzzy multiattribute group decision making based on cross entropy measure and Choquet integral. Int. J. Intell. Syst. 28(12), 1172–1195 (2013)
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We want to thank the anonymous reviewers for their constructive comments and suggestions, which have helped us improve this paper. This research is partially supported by a project funded by the China Postdoctoral Science Foundation under Award Number 2018M640058.
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Lin, S., Jia, L. & Wang, Y. Safety Assessment of Complex Electromechanical Systems Based on Hesitant Interval-Valued Intuitionistic Fuzzy Theory. Int. J. Fuzzy Syst. 21, 2405–2420 (2019). https://doi.org/10.1007/s40815-019-00729-4
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DOI: https://doi.org/10.1007/s40815-019-00729-4