Skip to main content
Log in

Reliability evaluation and importance analysis of structural systems considering dependence of multiple failure modes

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Many modern structural systems usually consist of multiple failure modes. One failure mode may affect other failure modes due to the same working environment and random input variables, meaning that these failure modes are not independent. The assumption of independence among failure modes simplifies the calculation of reliability of structural systems, but it adds approximation error. Different from the dependence between two failure modes, the dependence of multiple failure modes is more complicated. In this paper, on the basis of vine copula function, which is a flexible tool to describe the multivariate dependence, the dependence of multiple failure modes of the structural systems is mainly studied and analyzed. Then Rosenblatt transformation and Monte Carlo simulation method are utilized to evaluate the reliability of structural systems. Furthermore, in order to research the importance of failure modes, two indices combined with empirical copula functions are extended, which can quantitatively measure the importance of failure modes. The multiple failure modes can be ranked based on proposed importance analysis, and the ones that have greater influence on the system can be found out, thus simplifying the system analysis. Finally, in order to confirm the applicability and rationality of the proposed method, an engineering case about a mechanism system with five failure modes is presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Park C, Kim NH, Haftka RT (2015) The effect of ignoring dependence between failure modes on evaluating system reliability. Struct Multidiscipl Optim 52:251–268. https://doi.org/10.1007/s00158-015-1239-7

    Article  Google Scholar 

  2. Wei P, Liu F, Tang C (2018) Reliability and reliability-based importance analysis of structural systems using multiple response Gaussian process model. Reliab Eng Syst Saf 175:183–195. https://doi.org/10.1016/j.ress.2018.03.013

    Article  Google Scholar 

  3. Kaintura A, Spina D, Couckuyt I et al (2017) A Kriging and Stochastic Collocation ensemble for uncertainty quantification in engineering applications. Eng Comput 33:935–949. https://doi.org/10.1007/s00366-017-0507-0

    Article  Google Scholar 

  4. Khandelwal M (2011) Blast-induced ground vibration prediction using support vector machine. Eng Comput 27:193–200. https://doi.org/10.1007/s00366-010-0190-x

    Article  Google Scholar 

  5. Li J, Chen J, Fan W (2007) The equivalent extreme-value event and evaluation of the structural system reliability. Struct Saf 29:112–131. https://doi.org/10.1016/j.strusafe.2006.03.002

    Article  Google Scholar 

  6. Jia X, Shen J, Wang L, Li Z (2017) Vine copula constructions of higher-dimensional dependent reliability systems. Commun Stat Theory Methods 46:9126–9136. https://doi.org/10.1080/03610926.2016.1205620

    Article  MathSciNet  MATH  Google Scholar 

  7. Bichon BJ, McFarland JM, Mahadevan S (2011) Efficient surrogate models for reliability analysis of systems with multiple failure modes. Reliab Eng Syst Saf 96:1386–1395. https://doi.org/10.1016/j.ress.2011.05.008

    Article  Google Scholar 

  8. Sadoughi M, Li M, Hu C (2018) Multivariate system reliability analysis considering highly nonlinear and dependent safety events. Reliab Eng Syst Saf 180:189–200. https://doi.org/10.1016/j.ress.2018.07.015

    Article  Google Scholar 

  9. Li DQ, Zhang L, Tang XS et al (2015) Bivariate distribution of shear strength parameters using copulas and its impact on geotechnical system reliability. Comput Geotech 68:184–195. https://doi.org/10.1016/j.compgeo.2015.04.002

    Article  Google Scholar 

  10. Zhang J, Ma X, Zhao Y (2017) A stress-strength time-varying correlation interference model for structural reliability analysis using copulas. IEEE Trans Reliab 66:351–365. https://doi.org/10.1109/TR.2017.2694459

    Article  Google Scholar 

  11. Pan Z, Balakrishnan N, Sun Q, Zhou J (2013) Bivariate degradation analysis of products based on Wiener processes and copulas. J Stat Comput Simul 83:1316–1329. https://doi.org/10.1080/00949655.2012.658805

    Article  MathSciNet  MATH  Google Scholar 

  12. Peng W, Li Y-F, Yang Y-J et al (2016) Bivariate analysis of incomplete degradation observations based on inverse gaussian processes and copulas. IEEE Trans Reliab 65:624–639. https://doi.org/10.1109/TR.2015.2513038

    Article  Google Scholar 

  13. Eryilmaz S (2014) Multivariate copula based dynamic reliability modeling with application to weighted-k-out-of-n systems of dependent components. Struct Saf 51:23–28. https://doi.org/10.1016/j.strusafe.2014.05.004

    Article  Google Scholar 

  14. Bedford T, Cooke RM (2001) Probability density decomposition for conditionally dependent random variables modeled by vines. Ann Math Artif Intell 32:245–268. https://doi.org/10.1023/A:1016725902970

    Article  MathSciNet  MATH  Google Scholar 

  15. Bedford T, Cooke RM (2002) Vines–a new graphical model for dependent random variables. Ann Stat 30:1031–1068. https://doi.org/10.1214/aos/1031689016

    Article  MathSciNet  MATH  Google Scholar 

  16. Kurowicka D, Cooke R (2006) Uncertainty analysis with high dimensional dependence modelling. Wiley, Chichester, Hoboken

    Book  Google Scholar 

  17. Aas K, Czado C, Frigessi A, Bakken H (2009) Pair-copula constructions of multiple dependence. Insurance Math Econ 44:182–198. https://doi.org/10.1016/j.insmatheco.2007.02.001

    Article  MathSciNet  MATH  Google Scholar 

  18. Xu D, Wei Q, Elsayed EA et al (2017) Multivariate degradation modeling of smart electricity meter with multiple performance characteristics via vine copulas: multivariate degradation modeling of SEM via vine copulas. Qual Reliab Eng Int 33:803–821. https://doi.org/10.1002/qre.2058

    Article  Google Scholar 

  19. Jiang C, Zhang W, Han X et al (2015) A vine-copula-based reliability analysis method for structures with multidimensional correlation. J Mech Des 137:061405. https://doi.org/10.1115/1.4030179

    Article  Google Scholar 

  20. Czado C (2019) Analyzing dependent data with vine copulas: a practical guide with R. Springer, Cham

    Book  Google Scholar 

  21. Dutuit Y, Rauzy A (2015) On the extension of importance measures to complex components. Reliab Eng Syst Saf 142:161–168. https://doi.org/10.1016/j.ress.2015.04.016

    Article  Google Scholar 

  22. Wei P, Lu Z, Song J (2014) Moment-independent sensitivity analysis using copula: moment-independent sensitivity analysis using copula. Risk Anal 34:210–222. https://doi.org/10.1111/risa.12110

    Article  Google Scholar 

  23. Wei P, Lu Z, Song J (2015) Variable importance analysis: a comprehensive review. Reliab Eng Syst Saf 142:399–432. https://doi.org/10.1016/j.ress.2015.05.018

    Article  Google Scholar 

  24. Zhou C, Lu Z, Ren B, Cheng B (2014) Failure-mode importance measures in system reliability analysis. J Eng Mech 140:04014084. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000798

    Article  Google Scholar 

  25. He L, Lu Z, Li Xinyao (2018) Failure-mode importance measures in structural system with multiple failure modes and its estimation using copula. Reliab Eng Syst Saf 174:53–59. https://doi.org/10.1016/j.ress.2018.02.016

    Article  Google Scholar 

  26. Hohenbichler M, Rackwitz R (1982) First-order concepts in system reliability. Struct Saf 1:177–188. https://doi.org/10.1016/0167-4730(82)90024-8

    Article  Google Scholar 

  27. Nelsen RB (2006) An introduction to Copulas, 2. ed. 2006. Springer New York

  28. Joe H (1996) Families of m-variate distributions with given margins and m(m-1)/2 bivariate dependence parameters. In: Lecture notes-monograph series. Institute of Mathematical Statistics, Hayward, pp 120–141

  29. Akaike H (1992) Information theory and an extension of the maximum likelihood principle. In: Kotz S, Johnson NL (eds) Breakthroughs in statistics. Springer, New York, pp 610–624

    Chapter  Google Scholar 

  30. Aho K, Derryberry D, Peterson T (2014) Model selection for ecologists: the worldviews of AIC and BIC. Ecology 95:631–636. https://doi.org/10.1890/13-1452.1

    Article  Google Scholar 

  31. Keshtegar B, Kisi O (2017) M5 model tree and Monte Carlo simulation for efficient structural reliability analysis. Appl Math Model 48:899–910. https://doi.org/10.1016/j.apm.2017.02.047

    Article  MathSciNet  MATH  Google Scholar 

  32. Zeng P, Li T, Chen Y et al (2019) New collocation method for stochastic response surface reliability analyses. Eng Comput. https://doi.org/10.1007/s00366-019-00793-2

    Article  Google Scholar 

  33. Rosenblatt M (1952) Remarks on a multivariate transformation. Ann Math Statist 23:470–472. https://doi.org/10.1214/aoms/1177729394

    Article  MathSciNet  MATH  Google Scholar 

  34. Lebrun R, Dutfoy A (2009) A generalization of the Nataf transformation to distributions with elliptical copula. Probab Eng Mech 24:172–178. https://doi.org/10.1016/j.probengmech.2008.05.001

    Article  Google Scholar 

  35. Wang F, Li H (2018) Distribution modeling for reliability analysis: impact of multiple dependences and probability model selection. Appl Math Model 59:483–499. https://doi.org/10.1016/j.apm.2018.01.035

    Article  MathSciNet  MATH  Google Scholar 

  36. Lebrun R, Dutfoy A (2009) An innovating analysis of the Nataf transformation from the copula viewpoint. Probab Eng Mech 24:312–320. https://doi.org/10.1016/j.probengmech.2008.08.001

    Article  Google Scholar 

  37. Kuo W, Zuo MJ (2003) Optimal reliability modeling: principles and applications. Wiley, Hoboken.

    Google Scholar 

  38. Kuo W, Zhu X (2012) Importance measures in reliability, risk, and optimization: principles and applications. Wiley, Chichester

    Book  Google Scholar 

  39. Guo W, Cui W, Shi Y et al (2016) Function failure and failure boundary analysis for an aircraft lock mechanism. Eng Fail Anal 70:428–442. https://doi.org/10.1016/j.engfailanal.2016.10.003

    Article  Google Scholar 

  40. Shen L, Zhang Y, Song K, Song B (2019) Failure analysis of a lock mechanism with multiple dependent components based on two-phase degradation model. Eng Fail Anal 104:1076–1093. https://doi.org/10.1016/j.engfailanal.2019.06.035

    Article  Google Scholar 

  41. Pang H, Yu T, Song B (2016) Failure mechanism analysis and reliability assessment of an aircraft slat. Eng Fail Anal 60:261–279. https://doi.org/10.1016/j.engfailanal.2015.11.032

    Article  Google Scholar 

  42. Shu Zhen, Jirutitijaroen P (2011) Latin hypercube sampling techniques for power systems reliability analysis with renewable energy sources. IEEE Trans Power Syst 26:2066–2073. https://doi.org/10.1109/TPWRS.2011.2113380

    Article  Google Scholar 

  43. Bowman AW, Azzalini A (1997) Applied smoothing techniques for data analysis: the kernel approach with S-Plus illustrations. Clarendon Press, Oxford University Press, New York, Oxford

    MATH  Google Scholar 

  44. Dissmann J, Brechmann EC, Czado C, Kurowicka D (2013) Selecting and estimating regular vine copulae and application to financial returns. Comput Stat Data Anal 59:52–69. https://doi.org/10.1016/j.csda.2012.08.010

    Article  MathSciNet  MATH  Google Scholar 

  45. Nagler T, Schepsmeier U, Stoeber J et al (2019) VineCopula: statistical inference of vine copulas, R package version 2.2.0. https://CRAN.R-project.org/package=VineCopula

Download references

Acknowledgement

This paper has been supported by Natural Science Foundation of China (Grant no. 51675428).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yugang Zhang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shen, L., Zhang, Y., Song, B. et al. Reliability evaluation and importance analysis of structural systems considering dependence of multiple failure modes. Engineering with Computers 38, 1053–1070 (2022). https://doi.org/10.1007/s00366-020-01100-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-020-01100-0

Keywords