IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v124y2014icp56-67.html
   My bibliography  Save this article

Accounting for future redesign to balance performance and development costs

Author

Listed:
  • Villanueva, D.
  • Haftka, R.T.
  • Sankar, B.V.

Abstract

Most components undergo tests after they are designed and are redesigned if necessary. Tests help designers find unsafe and overly conservative designs, and redesign can restore safety or increase performance. In general, the expected changes to the performance and reliability of the design after the test and redesign are not considered. In this paper, we explore how modeling a future test and redesign provides a company an opportunity to balance development costs versus performance by simultaneously designing the design and the post-test redesign rules during the initial design stage. Due to regulations and tradition, safety margin and safety factor based design is a common practice in industry as opposed to probabilistic design. In this paper, we show that it is possible to continue to use safety margin based design, and employ probability solely to select safety margins and redesign criteria. In this study, we find the optimum safety margins and redesign criterion for an integrated thermal protection system. These are optimized in order to find a minimum mass design with minimal redesign costs. We observed that the optimum safety margin and redesign criterion call for an initially conservative design and use the redesign process to trim excess weight rather than restore safety. This would fit well with regulatory constraints, since regulations usually impose minimum safety margins.

Suggested Citation

  • Villanueva, D. & Haftka, R.T. & Sankar, B.V., 2014. "Accounting for future redesign to balance performance and development costs," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 56-67.
  • Handle: RePEc:eee:reensy:v:124:y:2014:i:c:p:56-67
    DOI: 10.1016/j.ress.2013.11.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832013003104
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2013.11.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Durga Rao, K. & Kushwaha, H.S. & Verma, A.K. & Srividya, A., 2007. "Quantification of epistemic and aleatory uncertainties in level-1 probabilistic safety assessment studies," Reliability Engineering and System Safety, Elsevier, vol. 92(7), pages 947-956.
    2. Benjamin P. Smarslok & Raphael T. Haftka & Laurent Carraro & David Ginsbourger, 2010. "Improving accuracy of failure probability estimates with separable Monte Carlo," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 4(4), pages 393-414.
    3. Rockafellar, R.T. & Royset, J.O., 2010. "On buffered failure probability in design and optimization of structures," Reliability Engineering and System Safety, Elsevier, vol. 95(5), pages 499-510.
    4. Möller, Niklas & Hansson, Sven Ove, 2008. "Principles of engineering safety: Risk and uncertainty reduction," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 798-805.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ahmed, Hussam & Chateauneuf, Alaa, 2014. "Optimal number of tests to achieve and validate product reliability," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 242-250.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. J. Park & T. P. Seager & P. S. C. Rao & M. Convertino & I. Linkov, 2013. "Integrating Risk and Resilience Approaches to Catastrophe Management in Engineering Systems," Risk Analysis, John Wiley & Sons, vol. 33(3), pages 356-367, March.
    2. Matthew Norton & Valentyn Khokhlov & Stan Uryasev, 2021. "Calculating CVaR and bPOE for common probability distributions with application to portfolio optimization and density estimation," Annals of Operations Research, Springer, vol. 299(1), pages 1281-1315, April.
    3. Xiaojiao Tong & Hailin Sun & Xiao Luo & Quanguo Zheng, 2018. "Distributionally robust chance constrained optimization for economic dispatch in renewable energy integrated systems," Journal of Global Optimization, Springer, vol. 70(1), pages 131-158, January.
    4. Daniel Eisenberg & Thomas Seager & David L. Alderson, 2019. "Rethinking Resilience Analytics," Risk Analysis, John Wiley & Sons, vol. 39(9), pages 1870-1884, September.
    5. Thilini V. Mahanama & Abootaleb Shirvani & Svetlozar Rachev, 2023. "The Financial Market of Indices of Socioeconomic Wellbeing," Papers 2303.05654, arXiv.org.
    6. Massimiliano Amarante, 2016. "A representation of risk measures," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 39(1), pages 95-103, April.
    7. Mafusalov, Alexander & Uryasev, Stan, 2016. "CVaR (superquantile) norm: Stochastic case," European Journal of Operational Research, Elsevier, vol. 249(1), pages 200-208.
    8. Tu Duong Le Duy & Laurence Dieulle & Dominique Vasseur & Christophe Bérenguer & Mathieu Couplet, 2013. "An alternative comprehensive framework using belief functions for parameter and model uncertainty analysis in nuclear probabilistic risk assessment applications," Journal of Risk and Reliability, , vol. 227(5), pages 471-490, October.
    9. Liu, Mingyuan & He, Wei & Ma, Ning & Zhu, Hailong & Zhou, Guohui, 2025. "A new reliability health status assessment model for complex systems based on belief rule base," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
    10. Zhu, Andy Yunlong & von Zedtwitz, Max & Assimakopoulos, Dimitris & Fernandes, Kiran, 2016. "The impact of organizational culture on Concurrent Engineering, Design-for-Safety, and product safety performance," International Journal of Production Economics, Elsevier, vol. 176(C), pages 69-81.
    11. R. Tyrrell Rockafellar & Johannes O. Royset, 2018. "Superquantile/CVaR risk measures: second-order theory," Annals of Operations Research, Springer, vol. 262(1), pages 3-28, March.
    12. Sarat Sivaprasad & Cameron A. MacKenzie, 2018. "The Hurwicz Decision Rule’s Relationship to Decision Making with the Triangle and Beta Distributions and Exponential Utility," Decision Analysis, INFORMS, vol. 15(3), pages 139-153, September.
    13. Linn Svegrup & Jonas Johansson & Henrik Hassel, 2019. "Integration of Critical Infrastructure and Societal Consequence Models: Impact on Swedish Power System Mitigation Decisions," Risk Analysis, John Wiley & Sons, vol. 39(9), pages 1970-1996, September.
    14. Abate, Arega Getaneh & Riccardi, Rossana & Ruiz, Carlos, 2021. "Contracts in electricity markets under EU ETS: A stochastic programming approach," Energy Economics, Elsevier, vol. 99(C).
    15. Rockafellar, R.T. & Royset, J.O. & Miranda, S.I., 2014. "Superquantile regression with applications to buffered reliability, uncertainty quantification, and conditional value-at-risk," European Journal of Operational Research, Elsevier, vol. 234(1), pages 140-154.
    16. L. Jeff Hong & Zhaolin Hu & Liwei Zhang, 2014. "Conditional Value-at-Risk Approximation to Value-at-Risk Constrained Programs: A Remedy via Monte Carlo," INFORMS Journal on Computing, INFORMS, vol. 26(2), pages 385-400, May.
    17. repec:cte:wsrepe:35425 is not listed on IDEAS
    18. Pertaia, Giorgi & Prokhorov, Artem & Uryasev, Stan, 2022. "A new approach to credit ratings," Journal of Banking & Finance, Elsevier, vol. 140(C).
    19. Guo, Tiexin & Wang, Hongji & Li, Jinglai & Wang, Hongqiao, 2024. "Sampling-based adaptive design strategy for failure probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    20. Johannes O. Royset & Roberto Szechtman, 2013. "Optimal Budget Allocation for Sample Average Approximation," Operations Research, INFORMS, vol. 61(3), pages 762-776, June.
    21. Ding, Rui & Liu, Zehua & Xu, Jintao & Meng, Fanpeng & Sui, Yang & Men, Xinhong, 2021. "A novel approach for reliability assessment of residual heat removal system for HPR1000 based on failure mode and effect analysis, fault tree analysis, and fuzzy Bayesian network methods," Reliability Engineering and System Safety, Elsevier, vol. 216(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:124:y:2014:i:c:p:56-67. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.