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Sensitivity Analysis for Systems under Epistemic Uncertainty with Probability Bounds Analysis

by Geng Feng
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 31
Year of Publication: 2018
Authors: Geng Feng

Geng Feng . Sensitivity Analysis for Systems under Epistemic Uncertainty with Probability Bounds Analysis. International Journal of Computer Applications. 179, 31 ( Apr 2018), 1-6. DOI=10.5120/ijca2018915892

@article{ 10.5120/ijca2018915892,
author = { Geng Feng },
title = { Sensitivity Analysis for Systems under Epistemic Uncertainty with Probability Bounds Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2018 },
volume = { 179 },
number = { 31 },
month = { Apr },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2018915892 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:57:04.781291+05:30
%A Geng Feng
%T Sensitivity Analysis for Systems under Epistemic Uncertainty with Probability Bounds Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 31
%P 1-6
%D 2018
%I Foundation of Computer Science (FCS), NY, USA

It is of paramount important to identify and rank the influence of components on the performance of interest. System sensitivity analysis provides a quantitative tool for accessing the importance of components within a specific system configuration. In practice, however, due to lack of information, there exist epistemic uncertainty within the components distribution parameters, which makes it is hard to estimate the reliability of the corresponding system. In this paper, survival signature is adopted to evaluate the system performance, and the area value of the probability box is introduced to reflect the epistemic uncertainty of the system. Also, in order to find out which component or components set is more sensitive to the system, the probability bounds analysis which bases on pinching method is used. Two case studies are presented to show the applicability of the approaches.

  1. Tazid Ali, Hrishikesh Boruah, and Palash Dutta. Sensitivity analysis in radiological risk assessment using probability bounds analysis. International Journal of Computer Applications, 44(17), 2012.
  2. Louis J.M. Aslett. Reliabilitytheory: Tools for structural reliability analysis. r package, 2012.
  3. Louis J.M. Aslett, Frank P.A. Coolen, and Simon P Wilson. Bayesian inference for reliability of systems and networks using the survival signature. Risk Analysis, 35(3):1640–1651, 2015.
  4. Michael Beer, Scott Ferson, and Vladik Kreinovich. Imprecise probabilities in engineering analyses. Mechanical systems and signal processing, 37(1):4–29, 2013.
  5. Daniele Binosi and Joannis Papavassiliou. Pinch technique: theory and applications. Physics Reports, 479(1):1–152, 2009.
  6. FPA Coolen. On the use of imprecise probabilities in reliability. Quality and Reliability Engineering International, 20(3):193–202, 2004.
  7. Frank P.A. Coolen and Tahani Coolen-Maturi. Generalizing the signature to systems with multiple types of components. In Complex Systems and Dependability, pages 115– 130. Springer, 2012.
  8. Frank PA Coolen and Tahani Coolen-Maturi. The structure function for system reliability as predictive (imprecise) probability. Reliability Engineering & System Safety, 154:180–187, 2016.
  9. Frank P.A. Coolen, Tahani Coolen-Maturi, and Abdullah H Al-Nefaiee. Nonparametric predictive inference for system reliability using the survival signature. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 228(5):437–448, 2014.
  10. Frank PA Coolen, Matthias CM Troffaes, and Thomas Augustin. Imprecise probability. In International encyclopedia of statistical science, pages 645–648. Springer, 2011.
  11. Arthur P Dempster. Upper and lower probabilities induced by a multivalued mapping. The annals of mathematical statistics, pages 325–339, 1967.
  12. Geng Feng, Edoardo Patelli, Michael Beer, and Frank PA Coolen. Imprecise system reliability and component importance based on survival signature. Reliability Engineering & System Safety, 150:116–125, 2016.
  13. S Ferson and S Donald. Probability bounds analysis. In International Conference on Probabilistic Safety Assessment and Management (PSAM4), pages 1203–1208, 1998.
  14. Scott Ferson and Lev R Ginzburg. Different methods are needed to propagate ignorance and variability. Reliability Engineering & System Safety, 54(2):133–144, 1996.
  15. Scott Ferson and Janos G Hajagos. Arithmetic with uncertain numbers: rigorous and (often) best possible answers. Reliability Engineering & System Safety, 85(1):135–152, 2004.
  16. Scott Ferson, Vladik Kreinovich, Lev Ginzburg, Davis S Myers, and Kari Sentz. Constructing probability boxes and Dempster-Shafer structures, volume 835. Sandia National Laboratories, 2002.
  17. Scott Ferson and W Troy Tucker. Sensitivity analysis using probability bounding. Reliability Engineering & System Safety, 91(10):1435–1442, 2006.
  18. Jon C Helton, Jay D Johnson, WL Oberkampf, and C´edric J Sallaberry. Sensitivity analysis in conjunction with evidence theory representations of epistemic uncertainty. Reliability Engineering & System Safety, 91(10):1414–1434, 2006.
  19. F Owen Hoffman and Jana S Hammonds. Propagation of uncertainty in risk assessments: the need to distinguish between uncertainty due to lack of knowledge and uncertainty due to variability. Risk analysis, 14(5):707–712, 1994.
  20. Durga Rao Karanki, Hari Shankar Kushwaha, Ajit Kumar Verma, and Srividya Ajit. Uncertainty analysis based on probability bounds (p-box) approach in probabilistic safety assessment. Risk Analysis, 29(5):662–675, 2009.
  21. Edward E Leamer. Sensitivity analyses would help. The American Economic Review, 75(3):308–313, 1985.
  22. Bernd M¨oller and Michael Beer. Fuzzy randomness: uncertainty in civil engineering and computational mechanics. Springer Science & Business Media, 2013.
  23. Edoardo Patelli, Diego A Alvarez, Matteo Broggi, and Marco de Angelis. An integrated and efficient numerical framework for uncertainty quantification: application to the nasa langley multidisciplinary uncertainty quantification challenge. In 16th AIAA Non-Deterministic Approaches Conference (SciTech 2014), pages 2014–1501, 2014.
  24. Edoardo Patelli and Geng Feng. Efficient simulation approaches for reliability analysis of large systems. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pages 129–140. Springer, 2016.
  25. Edoardo Patelli, Geng Feng, Frank PA Coolen, and Tahani Coolen-Maturi. Simulation methods for system reliability using the survival signature. Reliability Engineering & System Safety, 167:327–337, 2017.
  26. Kari Sentz and Scott Ferson. Probabilistic bounding analysis in the quantification of margins and uncertainties. Reliability Engineering & System Safety, 96(9):1126–1136, 2011.
  27. Glenn Shafer et al. A mathematical theory of evidence, volume 1. Princeton university press Princeton, 1976.
  28. Christophe Simon and Philippe Weber. Evidential networks for reliability analysis and performance evaluation of systems with imprecise knowledge. IEEE Transactions on Reliability, 58(1):69–87, 2009.
  29. Fulvio Tonon. Using random set theory to propagate epistemic uncertainty through a mechanical system. Reliability Engineering & System Safety, 85(1):169–181, 2004.
  30. Matthias CM Troffaes, Gero Walter, and Dana Kelly. A robust bayesian approach to modeling epistemic uncertainty in common-cause failure models. Reliability Engineering & System Safety, 125:13–21, 2014.
  31. Robert C Williamson and Tom Downs. Probabilistic arithmetic. i. numerical methods for calculating convolutions and dependency bounds. International journal of approximate reasoning, 4(2):89–158, 1990.
  32. ElmarWolfstetter et al. Stochastic dominance: theory and applications. Humboldt-Univ., Wirtschaftswiss. Fak., 1993.
  33. Qingyuan Zhang, Zhiguo Zeng, Enrico Zio, and Rui Kang. Probability box as a tool to model and control the effect of epistemic uncertainty in multiple dependent competing failure processes. Applied Soft Computing, 2016.
Index Terms

Computer Science
Information Sciences


Sensitivity Analysis Epistemic Uncertainty Probability Bounds Analysis Systems Reliability