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Bayesian Networks in Reliability Modeling and Assessment of Multi-state Systems

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Stochastic Models in Reliability, Network Security and System Safety (JHC80 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1102))

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Abstract

Multi-state is a characteristic of advanced engineering systems and products. The reliability of multi-state systems (MSSs) has been received considerable attentions since the middle of 1970s. Over the last decade, Bayesian networks (BNs), as an effective and efficient reasoning tool under uncertainty, have been intensively concerned in MSS reliability modeling and assessment. This chapter presented a holistic framework for MSS reliability modeling and assessment by BNs. Firstly, the basic characteristics of MSSs and BNs are reviewed. Secondly, the detailed procedures of constructing the BN models of diverse MSSs are provided. The corresponding dynamic Bayesian network (DBN) models are also constructed to characterize the degradation profiles of MSSs over time, as well as various dependencies among components. Thirdly, a reliability assessment method by fusing multi-level observation data is developed. The results show that the reliability modeling and assessment for MSSs by BNs are effective considerably.

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References

  1. Lisnianski, A., Levitin, G.: Multi-State System Reliability: Assessment, Optimization and Applications. World Scientific Publishing Company, Singapore (2003)

    Book  MATH  Google Scholar 

  2. Kuo, W., Zuo, M.J.: Optimal Reliability Modeling: Principles and Applications. Wiley, Hoboken (2003)

    Google Scholar 

  3. Lisnianski, A., Frenkel, I., Ding, Y.: Multi-State System Reliability Analysis and Optimization for Engineers and Industrial Managers. Springer, London (2010). https://doi.org/10.1007/978-1-84996-320-6

    Book  MATH  Google Scholar 

  4. Jiang, T., Liu, Y., Zheng, Y.X.: Optimal loading strategy for multi-state systems: Cumulative performance perspective. Appl. Math. Model. 74, 199–216 (2019)

    Article  MathSciNet  Google Scholar 

  5. Lisnianski, A., Frenkel, I., Karagrigoriou, A.: Recent Advances in Multi-State Systems Reliability: Theory and Applications. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63423-4

    Book  MATH  Google Scholar 

  6. Dimla Sr., D.E., Lister, P.M.: On-line metal cutting tool condition monitoring. II: tool-state classification using multi-layer perceptron neural networks. Int. J. Mach. Tools Manuf. 40(5), 769–781 (2000)

    Article  Google Scholar 

  7. Graves, T.L., Hamada, M.S., Klamann, R., Koehler, A., Martz, H.F.: A fully Bayesian approach for combining multi-level information in multi-state fault tree quantification. Reliab. Eng. Syst. Saf. 92(10), 1476–1483 (2007)

    Article  Google Scholar 

  8. Yeh, W.: A fast algorithm for searching all multi-state minimal cuts. IEEE Trans. Reliab. 57(4), 581–588 (2008)

    Article  Google Scholar 

  9. Lin, Y.: Network reliability of a time-based multistate network under spare routing with p minimal paths. IEEE Trans. Reliab. 60(1), 61–69 (2011)

    Article  Google Scholar 

  10. Shrestha, A., Xing, L., Dai, Y.: Decision diagram based methods and complexity analysis for multi-state systems. IEEE Trans. Reliab. 59(1), 145–161 (2010)

    Article  Google Scholar 

  11. Trivedi, K.S.: Probability and Statistics with Reliability, Queuing, and Computer Science Applications. Wiley, Hoboken (2016)

    Book  MATH  Google Scholar 

  12. Limnios, N., Oprişan, G.: Semi-Markov Processes and Reliability. Springer, Heidelberg (2001). https://doi.org/10.1007/978-1-4612-0161-8

    Book  MATH  Google Scholar 

  13. Limnios, N., Barbu, V.S.: Semi-Markov Chains and Hidden Semi-Markov Models toward Applications: Their use in Reliability and DNA Analysis. Springer, New York (2008). https://doi.org/10.1007/978-0-387-73173-5

    Book  MATH  Google Scholar 

  14. Levitin, G.: The Universal Generating Function in Reliability Analysis and Optimization. Springer, London (2005). https://doi.org/10.1007/1-84628-245-4

    Book  Google Scholar 

  15. Zio, E.: The Monte Carlo Simulation Method for System Reliability and Risk Analysis. Springer, London (2013). https://doi.org/10.1007/978-1-4471-4588-2

    Book  Google Scholar 

  16. Zuo, M.J., Zhigang, T.: Performance evaluation of generalized multi-state k-out-of-n systems. IEEE Trans. Reliab. 55(2), 319–327 (2006)

    Article  Google Scholar 

  17. Li, W., Zuo, M.J.: Reliability evaluation of multi-state weighted k-out-of-n systems. Reliab. Eng. Syst. Saf. 93(1), 160–167 (2008)

    Article  Google Scholar 

  18. Jensen, F.V., Nielsen, T.D.: Bayesian Networks and Decision Graphs. Springer, New York (2007). https://doi.org/10.1007/978-0-387-68282-2

    Book  MATH  Google Scholar 

  19. Jiang, T., Liu, Y.: Parameter inference for non-repairable multi-state system reliability models by multi-level observation sequences. Reliab. Eng. Syst. Saf. 166, 3–15 (2017)

    Article  Google Scholar 

  20. Scutari, M., Denis, J.-B.: Bayesian Networks with Examples in R. CRC Press, Boca Raton (2014)

    Book  MATH  Google Scholar 

  21. Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. The MIT Press, Massachusetts (2009)

    Google Scholar 

  22. Langseth, H., Portinale, L.: Bayesian networks in reliability. Reliab. Eng. Syst. Saf. 92(1), 92–108 (2007)

    Article  Google Scholar 

  23. Weber, P., Medina-Oliva, G., Simon, C., Iung, B.: Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Eng. Appl. Artif. Intell. 25(4), 671–682 (2012)

    Article  Google Scholar 

  24. Mkrtchyan, L., Podofillini, L., Dang, V.N.: Bayesian belief networks for human reliability analysis: A review of applications and gaps. Reliab. Eng. Syst. Saf. 139, 1–16 (2015)

    Article  Google Scholar 

  25. Kelly, D.L., Smith, C.L.: Bayesian inference in probabilistic risk assessment—the current state of the art. Reliab. Eng. Syst. Saf. 94(2), 628–643 (2009)

    Article  Google Scholar 

  26. Cai, B., Huang, L., Xie, M.: Bayesian networks in fault diagnosis. IEEE Trans. Ind. Inform. 13(5), 2227–2240 (2017)

    Article  Google Scholar 

  27. Cai, B., et al.: Application of Bayesian networks in reliability evaluation. IEEE Trans. Ind. Inform. 15(4), 2146–2157 (2019)

    Article  Google Scholar 

  28. Bobbio, A., Portinale, L., Minichino, M., Ciancamerla, E.: Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliab. Eng. Syst. Saf. 71(3), 249–260 (2001)

    Article  Google Scholar 

  29. Khakzad, N., Khan, F., Amyotte, P.: Safety analysis in process facilities: comparison of fault tree and Bayesian network approaches. Reliab. Eng. Syst. Saf. 96(8), 925–932 (2011)

    Article  Google Scholar 

  30. Montani, S., Portinale, L., Bobbio, A., Codetta-Raiteri, D.: Radyban: a tool for reliability analysis of dynamic fault trees through conversion into dynamic Bayesian networks. Reliab. Eng. Syst. Saf. 93(7), 922–932 (2008)

    Article  Google Scholar 

  31. Norrington, L., Quigley, J., Russell, A., Van der Meer, R.: Modelling the reliability of search and rescue operations with Bayesian belief networks. Reliab. Eng. Syst. Saf. 93(7), 940–949 (2008)

    Article  Google Scholar 

  32. Nordgård, D.E., Sand, K.: Application of Bayesian networks for risk analysis of MV air insulated switch operation. Reliab. Eng. Syst. Saf. 95(12), 1358–1366 (2010)

    Article  Google Scholar 

  33. Morales-Nápoles, O., Steenbergen, R.D.J.M.: Analysis of axle and vehicle load properties through Bayesian networks based on Weigh-in-Motion data. Reliab. Eng. Syst. Saf. 125, 153–164 (2014)

    Article  Google Scholar 

  34. Mi, J., Li, Y.-F., Peng, W., Huang, H.-Z.: Reliability analysis of complex multi-state system with common cause failure based on evidential networks. Reliab. Eng. Syst. Saf. 174, 71–81 (2018)

    Article  Google Scholar 

  35. Xiahou, T.F., Liu, Y., Jiang, T.: Extended composite importance measures for multi-state systems with epistemic uncertainty of state assignment. Mech. Syst. Sig. Process. 109, 305–329 (2018)

    Article  Google Scholar 

  36. Francis, R.A., Guikema, S.D., Henneman, L.: Bayesian belief networks for predicting drinking water distribution system pipe breaks. Reliab. Eng. Syst. Saf. 130, 1–11 (2014)

    Article  Google Scholar 

  37. Tang, K., Parsons, D.J., Jude, S.: Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system. Reliab. Eng. Syst. Saf. 186, 24–36 (2019)

    Article  Google Scholar 

  38. Rafiq, M.I., Chryssanthopoulos, M.K., Sathananthan, S.: Bridge condition modelling and prediction using dynamic Bayesian belief networks. Struct. Infrastruct. Eng. 11(1), 38–50 (2015)

    Article  Google Scholar 

  39. Cai, B., Liu, Y., Liu, Z., Tian, X., Dong, X., Yu, S.: Using Bayesian networks in reliability evaluation for subsea blowout preventer control system. Reliab. Eng. Syst. Saf. 108, 32–41 (2012)

    Article  Google Scholar 

  40. Cai, B., Liu, Y., Zhang, Y., Fan, Q., Yu, S.: Dynamic Bayesian networks based performance evaluation of subsea blowout preventers in presence of imperfect repair. Expert Syst. Appl. 40(18), 7544–7554 (2013)

    Article  Google Scholar 

  41. Liu, Z., Liu, Y.: A Bayesian network based method for reliability analysis of subsea blowout preventer control system. J. Loss Prev. Process Ind. 59, 44–53 (2019)

    Article  Google Scholar 

  42. Simon, C., Weber, P.: Evidential networks for reliability analysis and performance evaluation of systems with imprecise knowledge. IEEE Trans. Reliab. 58(1), 69–87 (2009)

    Article  Google Scholar 

  43. Cai, B., Liu, Y., Fan, Q.: A multiphase dynamic Bayesian networks methodology for the determination of safety integrity levels. Reliab. Eng. Syst. Saf. 150, 105–115 (2016)

    Article  Google Scholar 

  44. Zuo, L., Xiahou, T., Liu, Y.: Reliability assessment of systems subject to interval-valued probabilistic common cause failure by evidential networks. J. Intell. Fuzzy Syst. 36, 3711–3723 (2019)

    Article  Google Scholar 

  45. Li, M., Liu, J., Li, J., Uk Kim, B.: Bayesian modeling of multi-state hierarchical systems with multi-level information aggregation. Reliab. Eng. Syst. Saf. 124, 158–164 (2014)

    Article  Google Scholar 

  46. Si, S., Cai, Z., Sun, S., Zhang, S.: Integrated importance measures of multi-state systems under uncertainty. Comput. Ind. Eng. 59(4), 921–928 (2010)

    Article  Google Scholar 

  47. Jones, B., Jenkinson, I., Yang, Z., Wang, J.: The use of Bayesian network modelling for maintenance planning in a manufacturing industry. Reliab. Eng. Syst. Saf. 95(3), 267–277 (2010)

    Article  Google Scholar 

  48. Liu, X., Zheng, J., Fu, J., Nie, Z., Chen, G.: Optimal inspection planning of corroded pipelines using BN and GA. J. Pet. Sci. Eng. 163, 546–555 (2018)

    Article  Google Scholar 

  49. Wang, X., Zhang, Y., Wang, L., Wang, J., Lu, J.: Maintenance grouping optimization with system multi-level information based on BN lifetime prediction model. J. Manuf. Syst. 50, 201–211 (2019)

    Article  Google Scholar 

  50. BahooToroody, A., Abaei, M.M., Arzaghi, E., BahooToroody, F., De Carlo, F., Abbassi, R.: Multi-level optimization of maintenance plan for natural gas system exposed to deterioration process. J. Hazard. Mater. 362, 412–423 (2019)

    Article  Google Scholar 

  51. Luque, J., Straub, D.: Risk-based optimal inspection strategies for structural systems using dynamic Bayesian networks. Struct. Saf. 76, 68–80 (2019)

    Article  Google Scholar 

  52. Boudali, H., Dugan, J.B.: A discrete-time Bayesian network reliability modeling and analysis framework. Reliab. Eng. Syst. Saf. 87(3), 337–349 (2005)

    Article  Google Scholar 

  53. Boudali, H., Dugan, J.B.: A continuous-time Bayesian network reliability modeling, and analysis framework. IEEE Trans. Reliab. 55(1), 86–97 (2006)

    Article  Google Scholar 

  54. Khakzad, N., Landucci, G., Reniers, G.: Application of dynamic Bayesian network to performance assessment of fire protection systems during domino effects. Reliab. Eng. Syst. Saf. 167, 232–247 (2017)

    Article  Google Scholar 

  55. Rebello, S., Yu, H., Ma, L.: An integrated approach for system functional reliability assessment using dynamic Bayesian network and Hidden Markov model. Reliab. Eng. Syst. Saf. 180, 124–135 (2018)

    Article  Google Scholar 

  56. Amin, M.T., Khan, F., Imtiaz, S.: Dynamic availability assessment of safety critical systems using a dynamic Bayesian network. Reliab. Eng. Syst. Saf. 178, 108–117 (2018)

    Article  Google Scholar 

  57. Xu, Z., Mo, Y., Liu, Y., Jiang, T.: Reliability assessment of multi-state phased-mission systems by fusing observation data from multiple phases of operation. Mech. Syst. Sig. Process. 118, 603–622 (2019)

    Article  Google Scholar 

  58. Khakzad, N.: Modeling wildfire spread in wildland-industrial interfaces using dynamic Bayesian network. Reliab. Eng. Syst. Saf. 189, 165–176 (2019)

    Article  Google Scholar 

  59. Weber, P., Jouffe, L.: Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN). Reliab. Eng. Syst. Saf. 91(2), 149–162 (2006)

    Article  Google Scholar 

  60. Liu, Q., Pérès, F., Tchangani, A.: Object oriented Bayesian network for complex system risk assessment. IFAC-PapersOnLine 49(28), 31–36 (2016)

    Article  MathSciNet  Google Scholar 

  61. Cai, B., Liu, H., Xie, M.: A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks. Mech. Syst. Sig. Process. 80, 31–44 (2016)

    Article  Google Scholar 

  62. Sarwar, A., Khan, F., James, L., Abimbola, M.: Integrated offshore power operation resilience assessment using object oriented Bayesian network. Ocean Eng. 167, 257–266 (2018)

    Article  Google Scholar 

  63. Abimbola, M., Khan, F.: Resilience modeling of engineering systems using dynamic object-oriented Bayesian network approach. Comput. Ind. Eng. 130, 108–118 (2019)

    Article  Google Scholar 

  64. Bensi, M., Kiureghian, A.D., Straub, D.: Efficient Bayesian network modeling of systems. Reliab. Eng. Syst. Saf. 112, 200–213 (2013)

    Article  Google Scholar 

  65. Zhu, J., Collette, M.: A dynamic discretization method for reliability inference in dynamic Bayesian networks. Reliab. Eng. Syst. Saf. 138, 242–252 (2015)

    Article  Google Scholar 

  66. Zwirglmaier, K., Straub, D.: A discretization procedure for rare events in Bayesian networks. Reliab. Eng. Syst. Saf. 153, 96–109 (2016)

    Article  Google Scholar 

  67. Tien, I., Der Kiureghian, A.: Algorithms for Bayesian network modeling and reliability assessment of infrastructure systems. Reliab. Eng. Syst. Saf. 156, 134–147 (2016)

    Article  Google Scholar 

  68. Zheng, X., Yao, W., Xu, Y., Chen, X.: Improved compression inference algorithm for reliability analysis of complex multistate satellite system based on multilevel Bayesian network. Reliab. Eng. Syst. Saf. 189, 123–142 (2019)

    Article  Google Scholar 

  69. Xing, L.: Reliability evaluation of phased-mission systems with imperfect fault coverage and common-cause failures. IEEE Trans. Reliab. 56(1), 58–68 (2007)

    Article  Google Scholar 

  70. Xing, L., Levitin, G.: BDD-based reliability evaluation of phased-mission systems with internal/external common-cause failures. Reliab. Eng. Syst. Saf. 112, 145–153 (2013)

    Article  Google Scholar 

  71. Wang, C., Xing, L., Levitin, G.: Explicit and implicit methods for probabilistic common-cause failure analysis. Reliab. Eng. Syst. Saf. 131, 175–184 (2014)

    Article  Google Scholar 

  72. Wang, C., Xing, L., Levitin, G.: Probabilistic common cause failures in phased-mission systems. Reliab. Eng. Syst. Saf. 144, 53–60 (2015)

    Article  Google Scholar 

  73. Sun, Y., Ma, L., Mathew, J., Zhang, S.: An analytical model for interactive failures. Reliab. Eng. Syst. Saf. 91(5), 495–504 (2006)

    Article  Google Scholar 

  74. Dao, C.D., Zuo, M.J.: Selective maintenance for multistate series systems with s-dependent components. IEEE Trans. Reliab. 65(2), 525–539 (2016)

    Article  Google Scholar 

  75. Jackson, C., Mosleh, A.: Bayesian inference with overlapping data for systems with continuous life metrics. Reliab. Eng. Syst. Saf. 106, 217–231 (2012)

    Article  Google Scholar 

  76. Li, M., Hu, Q., Liu, J.: Proportional hazard modeling for hierarchical systems with multi-level information aggregation. IIE Trans. 46(2), 149–163 (2014)

    Article  Google Scholar 

  77. Liu, Y., Chen, C.: Dynamic reliability assessment for nonrepairable multistate systems by aggregating multilevel imperfect inspection data. IEEE Trans. Reliab. 66(2), 281–297 (2017)

    Article  MathSciNet  Google Scholar 

  78. Ghasemi, A., Yacout, S., Ouali, M.S.: Evaluating the reliability function and the mean residual life for equipment with unobservable states. IEEE Trans. Reliab. 59(1), 45–54 (2010)

    Article  Google Scholar 

  79. Liu, Y., Zuo, M.J., Li, Y., Huang, H.: Dynamic reliability assessment for multi-state systems utilizing system-level inspection data. IEEE Trans. Reliab. 64(4), 1287–1299 (2015)

    Article  Google Scholar 

  80. Liu, Y.W., Kapur, K.C.: Reliability measures for dynamic multistate nonrepairable systems and their applications to system performance evaluation. IIE Trans. 38(6), 511–520 (2006)

    Article  Google Scholar 

  81. Yontay, P., Pan, R.: A computational Bayesian approach to dependency assessment in system reliability. Reliab. Eng. Syst. Saf. 152, 104–114 (2016)

    Article  Google Scholar 

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Acknowledgements

The authors greatly acknowledge grant support from the National Natural Science Foundation of China under contract numbers 71771039 and 71922006.

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, People’s Republic of China

    Tao Jiang, Yi-Xuan Zheng & Yu Liu

  2. Center for System Reliability and Safety, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, People’s Republic of China

    Yu Liu

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  1. Tao Jiang
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Correspondence to Yu Liu .

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Editors and Affiliations

  1. School of Economics and Management, Beijing University of Technology, Beijing, China

    Quan-Lin Li

  2. School of Management Science and Engineering, Central University of Finance and Economics, Beijing, China

    Jinting Wang

  3. School of Economics and Management, Beijing University of Technology, Beijing, China

    Hai-Bo Yu

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Jiang, T., Zheng, YX., Liu, Y. (2019). Bayesian Networks in Reliability Modeling and Assessment of Multi-state Systems. In: Li, QL., Wang, J., Yu, HB. (eds) Stochastic Models in Reliability, Network Security and System Safety. JHC80 2019. Communications in Computer and Information Science, vol 1102. Springer, Singapore. https://doi.org/10.1007/978-981-15-0864-6_9

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  • DOI: https://doi.org/10.1007/978-981-15-0864-6_9

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