No Access

Reliability Assessment of Safety Systems with Asymmetrical Redundancy Architecture

Department of Maritime and Transport Technology (M&TT), Delft University of Technology, Mekelweg 2, 2628CD Delft, The Netherlands

E-mail Address: e.rogova88@gmail.com

Corresponding author.

Search for more papers by this author

Gabriel Lodewijks

Department of Maritime and Transport Technology (M&TT), Delft University of Technology, Mekelweg 2, 2628CD Delft, The Netherlands

E-mail Address: g.lodewijks@unsw.edu.au

Search for more papers by this author

, and

Eduardo Calixto

ECC (Eduardo Calixto Consultant), Ravensburgerstraße 12, 89079, Ulm, Germany

E-mail Address: eduardo.calixto@hotmail.com

Search for more papers by this author

https://doi.org/10.1142/S0218539318500067Cited by:0 (Source: Crossref)

Abstract

Application of an M-out-of-N redundancy architecture is a well-known measure for improving the reliability of safety systems. Most scientific papers addressing the reliability assessment of such systems consider a conventional homogeneous M-out-of-N redundancy architecture that is performed for identical channels. However, often in practice, an M-out-of-N redundancy architecture does not have identical channels. Reliability assessment of such heterogeneous systems (electrical/electronic and mechanical) with nonidentical channels and a combination of constant and nonconstant failure rates is considered in this paper. Such type of M-out-of-N redundancy architecture is introduced in this research as “asymmetrical redundancy”. It can be used for enhancing the reliability of old mechanical systems or for reducing mutual influence of channels and increase of diagnostic coverage. This paper also presents a new “window-based Markov method” for PFD $_{avg}$ $_{avg}$ (average probability of failure on demand) and PFH (average frequency of dangerous failures) calculation for systems with an asymmetrical redundancy architecture and compares the results with those obtained by using the steady-state semi-Markov method and Monte-Carlo simulation. The applicability of the developed method is demonstrated in a simple case study.

Keywords:

References

1. W. Kuo and X. Zhu, Importance Measures in Reliability, Risk, and Optimization: Principles and Applications (John Wiley & Sons, Chichester, UK, 2012). Crossref, Google Scholar
2. M. Rausand, Reliability of Safety-Critical Systems: Theory and Applications (John Wiley & Sons, Hoboken, NJ, 2014). Crossref, Google Scholar
3. H. Hecht, Systems Reliability and Failure Prevention (Artech House, Norwood, MA, 2004). Google Scholar
4. M. Rausand and A. Høyland, System Reliability Theory. Models, Statistical Methods, and Applications, 2nd edn. (John Wiley & Sons, Hoboken, NJ, 2004). Google Scholar
5. V. K. Sharma, M. Agarwal and K. Sen, Reliability evaluation and optimal design in heterogeneous multi-state series-parallel systems, Inf. Sci. 181 (2011) 362–378. Crossref, Web of Science, Google Scholar
6. P. Boddu and L. Xing, Redundancy allocation for k-out-of-n: G systems with mixed spare types, in Proc. Reliability and Maintainability Symposium (RAMS), Reno, NV (2012), pp. 1–6. Google Scholar
7. X. Li and W. Ding, Optimal allocation of active redundancies to k-out-of-n systems with heterogeneous components, J. Appl. Prob. 47 (2010) 254–263. Crossref, Web of Science, Google Scholar
8. Y. Wang and L. Li, Heterogeneous redundancy allocation for series-parallel multi-state systems using hybrid particle swarm optimization and local search, IEEE Trans. Syst. Man Cybern. A: Syst. Humans 42(2) (2012) 464–474. Crossref, Web of Science, Google Scholar
9. IEC 61511-1. Functional safety — Safety instrumented systems for the process industry sector — Part 1: Framework, definitions, system, hardware and software requirements, 2003. Google Scholar
10. P. Pozsgai, W. Neher and B. Bertsche, Models to consider dependence in reliability calculation for systems consisting of mechanical components, in Proc. 3rd International Conference on Mathematical Methods in Reliability (MMR), Trondheim, Norway (2002), pp. 539–542. Google Scholar
11. IEC 61508-7. Functional safety of electrical/electronic/programmable electronic safety-related systems — Part 7: Overview of techniques and measures (International Electrotechnical Commission, Geneva, 2010). Google Scholar
12. IEC 61508-6. Functional safety of electrical/electronic/programmable electronic safety-related systems. Part 6: Guidelines on the application of IEC 61508-2 and IEC 61508-3 (International Electrotechnical Commission, Geneva, 2010). Google Scholar
13. IEC 62061. Safety of machinery — Functional safety of safety-related electrical, electronic and programmable electronic control systems (International Electrotechnical Commission, Geneva, 2005). Google Scholar
14. IEC 61810-2. Electromechanical elementary relays — Part 2: Reliability (International Electrotechnical Commission, Geneva, 2011). Google Scholar
15. F. P. Santos, A. P. Teixeira and C. Guedes Soares, An age-based preventive maintenance for offshore wind turbines, in Proc. European Safety and Reliability Conference (ESREL), Wroclaw, Poland (2014), pp. 1147–1155. Google Scholar
16. P. Dersin, A. Péronne and C. Arroum, Selecting test and maintenance strategies to achieve availability target with lowest life-cycle cost, in Proc. Reliability and Maintainability Symposium (RAMS), Las Vegas, NV, USA (2008), pp. 301–306. Google Scholar
17. R. Kumar and A. Jackson, Accurate reliability modeling using Markov analysis with non-constant hazard rates, in Proc. IEEE Aerospace Conference, Big Sky, MT, USA (2009), pp. 1–7. Google Scholar
18. J. Chudoba, Modelling of dynamical dependability by using stochastic processes, in Proc. European Safety and Reliability Conference (ESREL), Troyes, France (2011), pp. 2045–2049. Google Scholar
19. E. Rogova, G. Lodewijks and M. A. Lundteigen, Analytical formulas of PFD calculation for systems with non-constant failure rates, in Proc. European Safety and Reliability Conference (ESREL), Zurich, Switzerland (2015), pp. 1699–1707. Google Scholar
20. A. Hildebrandt, Calculating the “probability of failure on demand” (PFD) of complex structures by means of Markov models, in Proc. 4th European Conference on Electrical and Instrumentation Applications in the Petroleum and Chemical Industry, Paris, France (2007), pp. 1–5. Google Scholar
21. B. Harlamov, Continuous Semi-Markov Processes (John Wiley & Sons, Hoboken, NJ, 2008). Crossref, Google Scholar
22. M. Hellmich, Semi-Markov embeddable reliability structures and applications to load-sharing k-out-of-n systems. Int. J. Reliab. Qual. Saf. Eng. 20(2) (2013) 1–21. Link, Google Scholar
23. N. Limnios and G. Oprisan, Semi-Markov Processes and Reliability (Birkhäuser, Boston, 2001). Crossref, Google Scholar
24. G. Kumar, V. Jain and O. P. Gandhi, Availability analysis of repairable mechanical systems using analytical semi-Markov approach, Qual. Eng. 25(2) (2013) 97–107. Crossref, Web of Science, Google Scholar
25. L. F. S. Oliveira, PFD of higher-order configurations of SIS with partial stroke testing capability, in Proc. European Safety and Reliability Conference (ESREL), Valencia, Spain, (2008), pp. 1919–1928. Google Scholar
26. IEC 61508-4, Functional safety of electrical/electronic/programmable electronic safety-related systems. Part 4: Definitions and abbreviations (International Electrotechnical Commission, Geneva, 2010). Google Scholar
27. S. M. Ross, Stochastic Processes, 2nd edn. (John Wiley & Sons, New York, 1996). Google Scholar
28. P. Roettjer, Life testing and reliability predictions for electromechanical relays, Eval. Eng. 43(6) (2004) 58–61. Google Scholar
29. IXYS Integrated Circuits Division. Advantages of solid-state relays over electro-mechanical relays. Application Note: AN-145-R03, USA (2014). Google Scholar