Advertisement

A Multiphase Dynamic Bayesian Network Methodology for the Determination of Safety Integrity Levels

  • Baoping CaiEmail author
  • Yonghong Liu
  • Zengkai Liu
  • Yuanjiang Chang
  • Lei Jiang
Chapter

Abstract

A novel safety integrity level (SIL) determination methodology based on multiphase dynamic Bayesian networks (MDBNs) for safety instrumented systems is proposed. Proof test interval phase and proof test phase are modeled separately using dynamic Bayesian networks and integrated together to form the MDBNs. The unified structure models of MDBNs for k-out-of-n architectures are constructed, and the procedures of automatic creation of conditional probability tables are developed. The target failure measures, that is, probability of failure on demand, average probability of failure on demand, probability of failing safely, average probability of failing safely, and SIL of safety instrumented systems operating in a low-demand mode, are evaluated using the proposed MDBNs. The effects of time interval of MDBNs, common cause weight, imperfect proof test, and repair on model precision are researched. User-friendly SIL determination software is developed by using MATLAB GUI to assist engineers in determining the SIL value.

Keywords

Multiphase dynamic Bayesian networks Safety integrity level Safety instrumented system KooM architecture KooMD architecture 

Notes

Acknowledgements

The authors wish to acknowledge the financial support of Hong Kong Scholars Program (No. XJ2014004), National Natural Science Foundation of China (No. 51309240), Specialized Research Fund for the Doctoral Program of Higher Education (No. 20130133120007), China Postdoctoral Science Foundation (No. 2015M570624), Applied Basic Research Programs of Qingdao (No. 14-2-4-68-jch), Science and Technology Project of Huangdao District (No. 2014-1-48), Fundamental Research Funds for the Central Universities (No. 14CX02197A), Theme-based Research Scheme of University Grants Council (No. T32-101/15-R), and Key Project of National Natural Science Foundation of China (No. 71532008).

References

  1. 1.
    B. Cai, Y. Liu, Z. Liu, F. Wang, X. Tian, Y. Zhang, Development of an automatic subsea blowout preventer stack control system using PLC based SCADA. ISA Trans. 51(1), 198–207 (2012)CrossRefGoogle Scholar
  2. 2.
    X. Iturbe, A. Ebrahim, K. Benkrid, C. Hong, T. Arslan, J. Perez, D. Keymeulen, M.D. Santambrogio, R3TOS-based autonomous fault-tolerant systems. IEEE Micro 34(6), 20–30 (2014)CrossRefGoogle Scholar
  3. 3.
    R. Hammett, Flight-critical distributed systems: Design considerations [avionics]. IEEE Aerosp. Electron. Syst. Mag. 18(6), 30–36 (2003)CrossRefGoogle Scholar
  4. 4.
    IEC 61508, Electric/Electronic/Programmable Electronic safety-related systems, parts 1–7. Technical Report, International Electrotechnical Commission (May 2010)Google Scholar
  5. 5.
    IEC 61511, Functional safety: Safety instrumented systems for the process industry sector, parts 1–3. Technical Report, International Electrotechnical Commission (Mar 2003)Google Scholar
  6. 6.
    IEC 61513, Nuclear Power Plants: Instrumentation and Control Important to Safety, General Requirements for Systems (Oct 2011)Google Scholar
  7. 7.
    IEC 62061, Safety of Machinery: Functional Safety of Electrical, Electronic and Programmable Electronic Control Systems (Jan 2005)Google Scholar
  8. 8.
    EN 50129, Railway Applications: Communications, Signaling and Processing Systems, Safety Related Electronic Systems for Signaling (May 2002)Google Scholar
  9. 9.
    ISO 26262, Road Vehicles: Functional Safety (Nov 2011)Google Scholar
  10. 10.
    M. Catelani, L. Ciani, V. Luongo, A simplified procedure for the analysis of safety instrumented systems in the process industry application. Microelectron. Reliab. 51, 9–11 (2011)CrossRefGoogle Scholar
  11. 11.
    I.W. Soro, M. Nourelfath, D. Aït-Kadi, Performance evaluation of multi-state degraded systems with minimal repairs and imperfect preventive maintenance. Reliab. Eng. Syst. Saf. 95, 65–69 (2010)CrossRefGoogle Scholar
  12. 12.
    O. Gemikonakli, E. Ever, A. Kocyigit, Approximate solution for two stage open networks with Markov-modulated queues minimizing the state space explosion problem. J. Comput. Appl. Math. 223, 519–533 (2009)MathSciNetCrossRefGoogle Scholar
  13. 13.
    S.K. Kim, Y.S. Kim, An evaluation approach using a HARA and FMEDA for the hardware SIL. J. Loss Prev. Process Ind. 26, 1212–1220 (2013)CrossRefGoogle Scholar
  14. 14.
    Y. Dutuit, F. Innal, A. Rauzy, J.P. Signoret, Probabilistic assessments in relationship with safety integrity levels by using fault trees. Reliab. Eng. Syst. Saf. 93, 1867–1876 (2008)CrossRefGoogle Scholar
  15. 15.
    K. Chang, S. Kim, D. Chang, J. Ahn, E. Zio, Uncertainty analysis for target SIL determination in the offshore industry. J. Loss Prev. Process Ind. 34, 151–162 (2015)CrossRefGoogle Scholar
  16. 16.
    M. Khalil, M.A. Abdou, M.S. Mansour, H.A. Farag, M.E. Ossman, A cascaded fuzzy-LOPA risk assessment model applied in natural gas industry. J. Loss Prev. Process Ind. 25, 877–882 (2012)CrossRefGoogle Scholar
  17. 17.
    W. Mechri, C. Simon, K. BenOthman, Switching Markov chains for a holistic modeling of SIS unavailability. Reliab. Eng. Syst. Saf. 133, 212–222 (2015)CrossRefGoogle Scholar
  18. 18.
    L. Ding, H. Wang, K. Kang, K. Wang, A novel method for SIL verification based on system degradation using reliability block diagram. Reliab. Eng. Syst. Saf. 132, 36–45 (2014)CrossRefGoogle Scholar
  19. 19.
    R. Nait-Said, F. Zidani, N. Ouzraoui, Modified risk graph method using fuzzy rule-based approach. J. Hazard. Mater. 164, 651–658 (2009)CrossRefGoogle Scholar
  20. 20.
    T. Daemi, A. Ebrahimi, M. Fotuhi-Firuzabad, Constructing the Bayesian network for components reliability importance ranking in composite power systems. Int. J. Electr. Power Energy Syst. 43, 474–480 (2012)CrossRefGoogle Scholar
  21. 21.
    Y.D. Shu, J.S. Zhao, A simplified Markov-based approach for safety integrity level verification. J. Loss Prev. Process Ind. 29, 262–266 (2014)CrossRefGoogle Scholar
  22. 22.
    M. Sallak, C. Simon, J.-F. Aubry, A fuzzy probabilistic approach for determining safety integrity level. IEEE Trans. Fuzzy Syst. 16, 239–248 (2008)CrossRefGoogle Scholar
  23. 23.
    H. Jahanian, Generalizing PFD formulas of IEC 61508 for KooN architectures. ISA Trans. 55, 168–174 (2015)CrossRefGoogle Scholar
  24. 24.
    R. Ouache, M.N. Kabir, A.A. Adham, A reliability model for safety instrumented system. Saf. Sci. 80, 264–273 (2015)CrossRefGoogle Scholar
  25. 25.
    F. Innal, Y. Dutuit, M. Chebila, Safety and operational integrity evaluation and design optimization of safety instrumented systems. Reliab. Eng. Syst. Saf. 134, 32–50 (2015)CrossRefGoogle Scholar
  26. 26.
    K. Tsilipanos, I. Neokosmidis, D. Varoutas, A system of systems framework for the reliability assessment of telecommunications networks. IEEE Syst. J. 7, 114–124 (2013)CrossRefGoogle Scholar
  27. 27.
    O. Doguc, R.-M.J. Emmanuel, An automated method for estimating reliability of grid systems using Bayesian networks. Reliab. Eng. Syst. Saf. 104, 96–105 (2012)CrossRefGoogle Scholar
  28. 28.
    Y. Jiang, H.H. Zhang, X.Y. Song, X. Jiao, W.N.N. Hung, M. Gu, J.G. Sun, Bayesian-network-based reliability analysis of PLC systems. IEEE Trans. Ind. Electron 60, 5325–5336 (2013)CrossRefGoogle Scholar
  29. 29.
    L.M. Zhang, X.G. Wu, M.J. Skibniewski, J.B. Zhong, Y.J. Lu, Bayesian-network-based safety risk analysis in construction projects. Reliab. Eng. Syst. Saf. 131, 29–39 (2014)CrossRefGoogle Scholar
  30. 30.
    T. Daemi, A. Ebrahimi, Detailed reliability assessment of composite power systems considering load variation and weather conditions using the Bayesian network. Int. Trans. Electr. Energy Syst. 24, 305–317 (2014)CrossRefGoogle Scholar
  31. 31.
    P. Baraldi, L. Podofillini, L. Mkrtchyan, E. Zio, V.N. Dang, Comparing the treatment of uncertainty in Bayesian networks and fuzzy expert systems used for a human reliability analysis application. Reliab. Eng. Syst. Saf. 138, 176–193 (2015)CrossRefGoogle Scholar
  32. 32.
    A. O’Connora, A. Moslehb, A general cause based methodology for analysis of common cause and dependent failures in system risk and reliability assessments. Reliab. Eng. Syst. Saf. 145, 341–350 (2016)CrossRefGoogle Scholar
  33. 33.
    B.P. Cai, Y.H. Liu, Z.K. Liu, X.J. Tian, X. Dong, S.L. Yu, Using Bayesian networks in reliability evaluation for subsea blowout preventer control system. Reliab. Eng. Syst. Saf. 108, 32–41 (2012)CrossRefGoogle Scholar
  34. 34.
    B. Cai, Y. Liu, Y. Ma, L. Huang, Z. Liu, A framework for the reliability evaluation of grid-connected photovoltaic systems in the presence of intermittent faults. Energy 93, 1308–1320 (2015)CrossRefGoogle Scholar
  35. 35.
    B. Cai, Y. Liu, Y. Ma, Z. Liu, Y. Zhou, J. Sun, Real-time reliability evaluation methodology based on dynamic Bayesian networks: A case study of a subsea pipe ram BOP system. ISA Trans. 58, 595–604 (2015)CrossRefGoogle Scholar
  36. 36.
    P.A.P. Ramírez, I.B. Utne, Use of dynamic Bayesian networks for life extension assessment of ageing systems. Reliab. Eng. Syst. Saf. 133, 119–136 (2015)CrossRefGoogle Scholar
  37. 37.
    F. Flammini, S. Marrone, N. Mazzocca, V. Vittorini, A new modeling approach to the safety evaluation of N-modular redundant computer systems in presence of imperfect maintenance. Reliab. Eng. Syst. Saf. 94(9), 1422–1432 (2009)CrossRefGoogle Scholar
  38. 38.
    F. Flammini, S. Marrone, N. Mazzocca, R. Nardone, V. Vittorini, Using Bayesian Networks to evaluate the trustworthiness of ‘2 out of 3’ decision fusion mechanisms in multi-sensor applications. IFAC-Pap. OnLine 48(21), 682–687 (2015)CrossRefGoogle Scholar
  39. 39.
    P. Weber, C. Simon, D. Theilliol, Reconfiguration of over-actuated consecutive-k-out-of-n: F systems based on Bayesian network reliability model. ACD. 302 (2010)Google Scholar
  40. 40.
    H. Guo, X. Yang, Automatic creation of Markov models for reliability assessment of safety instrumented systems. Reliab. Eng. Syst. Saf. 93, 807–815 (2008)CrossRefGoogle Scholar
  41. 41.
    J. Jin, L. Pang, S. Zhao, B. Hu, Quantitative assessment of probability of failing safely for the safety instrumented system using reliability block diagram method. Ann. Nucl. Energy 77, 30–34 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Baoping Cai
    • 1
    Email author
  • Yonghong Liu
    • 1
  • Zengkai Liu
    • 1
  • Yuanjiang Chang
    • 1
  • Lei Jiang
    • 2
  1. 1.China University of PetroleumQingdaoChina
  2. 2.Southwest Jiaotong UniversityChengduChina

Personalised recommendations