International Journal of Fuzzy Systems

, Volume 21, Issue 8, pp 2405–2420 | Cite as

Safety Assessment of Complex Electromechanical Systems Based on Hesitant Interval-Valued Intuitionistic Fuzzy Theory

  • Shuai LinEmail author
  • Limin Jia
  • Yanhui Wang


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.


System safety Failure propagation Network theory Failure consequence Complex electromechanical system 



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.


  1. 1.
    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)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    Givoni, M.: Development and impact of the modern high-speed train: a review. Transport. Rev. 26(5), 593–611 (2006)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    Sharvia, S., Papadopoulos, Y.: Integrating model checking with HiP-HOPS in model-based safety analysis. Reliab. Eng. Syst. Safe. 135, 64–80 (2015)Google Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    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)Google Scholar
  17. 17.
    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)MathSciNetGoogle Scholar
  18. 18.
    Luo, Y., van den Brand, M.: Metrics design for safety assessment. Inform. Softw. Tech. 73, 151–163 (2016)Google Scholar
  19. 19.
    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)Google Scholar
  20. 20.
    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)Google Scholar
  21. 21.
    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)Google Scholar
  22. 22.
    Liu, X., An, S.: Failure propagation analysis of aircraft engine systems based on complex network. Procedia Eng. 80, 506–521 (2014)Google Scholar
  23. 23.
    Lu, X., Liu, M.: Hazard rate function in dynamic environment. Reliab. Eng. Syst. Safe. 130, 50–60 (2014)Google Scholar
  24. 24.
    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)Google Scholar
  25. 25.
    Zio, E.: Challenges in the vulnerability and risk analysis of critical infrastructures. Reliab. Eng. Syst. Safe. 152, 137–150 (2016)Google Scholar
  26. 26.
    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)Google Scholar
  27. 27.
    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)Google Scholar
  28. 28.
    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)Google Scholar
  29. 29.
    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)Google Scholar
  30. 30.
    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)Google Scholar
  31. 31.
    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)Google Scholar
  32. 32.
    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)Google Scholar
  33. 33.
    Zhang, Z.: Interval-valued intuitionistic hesitant fuzzy aggregation operators and their application in group decision-making. J. Appl. Math. 2013, 33 (2013)zbMATHGoogle Scholar
  34. 34.
    Farhadinia, B.: Information measures for hesitant fuzzy sets and interval-valued hesitant fuzzy sets. Inf. Sci. 240, 129–144 (2013)MathSciNetzbMATHGoogle Scholar
  35. 35.
    Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. 25(6), 529–539 (2010)zbMATHGoogle Scholar
  36. 36.
    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)MathSciNetzbMATHGoogle Scholar
  37. 37.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 52(3), 1059–1069 (2010)Google Scholar
  38. 38.
    Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: generalizing degree and shortest paths. Soc. Netw. 32(3), 245–251 (2010)Google Scholar
  39. 39.
    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)Google Scholar
  40. 40.
    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)Google Scholar
  41. 41.
    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)Google Scholar

Copyright information

© Taiwan Fuzzy Systems Association 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Rail Traffic Control and SafetyBeijing Jiaotong UniversityBeijingChina

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