IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v103y2012icp94-101.html
   My bibliography  Save this article

A physics-of-failure based reliability and maintenance modeling framework for stent deployment and operation

Author

Listed:
  • Keedy, Elias
  • Feng, Qianmei

Abstract

Reliability study of stents becomes extremely important due to the high demand on these devices to counteract the effects of atherosclerosis. Based on the physics-of-failure mechanisms, we propose a probabilistic reliability and maintenance modeling framework for stent deployment and operation. The fracture-mechanics-based approach in literature provides a rational basis for quantitative evaluation of damaging effects from two dominating failure modes of stents: (1) delayed failures or fatigue crack growth due to cyclic stresses, and (2) instantaneous failures due to single-event overloads. We develop the system reliability function using probabilistic degradation and random shock models. The developed system reliability model of stents is then incorporated in the optimization of a unique two-phase maintenance policy for achieving persistent patient outcomes. A numerical example is used to illustrate the results, where data in literature are used to analyze the reliability and optimize the maintenance schedule for stents. The developed reliability and maintenance models and analysis tools for stents provide fundamentally new perspectives on the application of reliability concepts to evolving medical devices.

Suggested Citation

  • Keedy, Elias & Feng, Qianmei, 2012. "A physics-of-failure based reliability and maintenance modeling framework for stent deployment and operation," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 94-101.
    • Handle: RePEc:eee:reensy:v:103:y:2012:i:c:p:94-101
    DOI: 10.1016/j.ress.2012.03.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832012000397
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2012.03.005?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. Zhu, Y. & Elsayed, E.A. & Liao, H. & Chan, L.Y., 2010. "Availability optimization of systems subject to competing risk," European Journal of Operational Research, Elsevier, vol. 202(3), pages 781-788, May.
    2. Bae, Suk Joo & Kuo, Way & Kvam, Paul H., 2007. "Degradation models and implied lifetime distributions," Reliability Engineering and System Safety, Elsevier, vol. 92(5), pages 601-608.
    3. Toshio Nakagawa, 2007. "Shock and Damage Models in Reliability Theory," Springer Series in Reliability Engineering, Springer, number 978-1-84628-442-7, March.
    4. Cadini, F. & Zio, E. & Avram, D., 2009. "Model-based Monte Carlo state estimation for condition-based component replacement," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 752-758.
    5. Khac Tuan Huynh & Anne Barros & Christophe Bérenguer & Inma T. Castro, 2011. "A periodic inspection and replacement policy for systems subject to competing failure modes due to degradation and traumatic events," Post-Print hal-00790728, HAL.
    6. Huynh, K.T. & Barros, A. & Bérenguer, C. & Castro, I.T., 2011. "A periodic inspection and replacement policy for systems subject to competing failure modes due to degradation and traumatic events," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 497-508.
    7. Wang, Hongzhou, 2002. "A survey of maintenance policies of deteriorating systems," European Journal of Operational Research, Elsevier, vol. 139(3), pages 469-489, June.
    8. Deloux, E. & Castanier, B. & Bérenguer, C., 2009. "Predictive maintenance policy for a gradually deteriorating system subject to stress," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 418-431.
    9. van Noortwijk, J.M., 2009. "A survey of the application of gamma processes in maintenance," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 2-21.
    10. Liao, Haitao & Elsayed, Elsayed A. & Chan, Ling-Yau, 2006. "Maintenance of continuously monitored degrading systems," European Journal of Operational Research, Elsevier, vol. 175(2), pages 821-835, December.
    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. Lin, Yan-Hui & Li, Yan-Fu & Zio, Enrico, 2018. "A comparison between Monte Carlo simulation and finite-volume scheme for reliability assessment of multi-state physics systems," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 1-11.
    2. Liu, Jie & Zio, Enrico, 2017. "System dynamic reliability assessment and failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 21-36.
    3. Bae, Suk Joo & Yuan, Tao & Ning, Shuluo & Kuo, Way, 2015. "A Bayesian approach to modeling two-phase degradation using change-point regression," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 66-74.
    4. Gu, Hang-Hang & Wang, Run-Zi & Tang, Min-Jin & Zhang, Xian-Cheng & Tu, Shan-Tung, 2024. "Data-physics-model based fatigue reliability assessment methodology for high-temperature components and its application in steam turbine rotor," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    5. Jiang, Shan & Li, Yan-Fu, 2021. "Dynamic Reliability Assessment of Multi-cracked Structure under Fatigue Loading via Multi-State Physics Model," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    6. Chen, Ying & Yang, Liu & Ye, Cui & Kang, Rui, 2015. "Failure mechanism dependence and reliability evaluation of non-repairable system," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 273-283.
    7. Yousefi, Nooshin & Coit, David W. & Song, Sanling, 2020. "Reliability analysis of systems considering clusters of dependent degrading components," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    8. Li, Ying Yi & Chen, Ying & Yuan, Zeng Hui & Tang, Ning & Kang, Rui, 2017. "Reliability analysis of multi-state systems subject to failure mechanism dependence based on a combination method," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 109-123.
    9. Jin, Guang & Matthews, David E. & Zhou, Zhongbao, 2013. "A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries inspacecraft," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 7-20.
    10. An, Zongwen & Sun, Daoming, 2017. "Reliability modeling for systems subject to multiple dependent competing failure processes with shock loads above a certain level," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 129-138.
    11. Sakurahara, Tatsuya & O'Shea, Nicholas & Cheng, Wen-Chi & Zhang, Sai & Reihani, Seyed & Kee, Ernie & Mohaghegh, Zahra, 2019. "Integrating renewal process modeling with Probabilistic Physics-of-Failure: Application to Loss of Coolant Accident (LOCA) frequency estimations in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    12. Wenjie Dong & Sifeng Liu & Zhigeng Fang & Yingsai Cao & Ye Ding, 2019. "A model based on hidden graphic evaluation and review technique network to evaluate reliability and lifetime of multi-state systems," Journal of Risk and Reliability, , vol. 233(3), pages 369-378, June.

    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. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    2. de Jonge, Bram & Scarf, Philip A., 2020. "A review on maintenance optimization," European Journal of Operational Research, Elsevier, vol. 285(3), pages 805-824.
    3. Huynh, K.T. & Grall, A. & Bérenguer, C., 2017. "Assessment of diagnostic and prognostic condition indices for efficient and robust maintenance decision-making of systems subject to stress corrosion cracking," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 237-254.
    4. Zhengxin Zhang & Xiaosheng Si & Changhua Hu & Xiangyu Kong, 2015. "Degradation modeling–based remaining useful life estimation: A review on approaches for systems with heterogeneity," Journal of Risk and Reliability, , vol. 229(4), pages 343-355, August.
    5. Zhang, Aibo & Zhang, Tieling & Barros, Anne & Liu, Yiliu, 2020. "Optimization of maintenances following proof tests for the final element of a safety-instrumented system," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    6. Huynh, K.T., 2021. "An adaptive predictive maintenance model for repairable deteriorating systems using inverse Gaussian degradation process," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    7. Hai-Kun Wang & Yan-Feng Li & Yu Liu & Yuan-Jian Yang & Hong-Zhong Huang, 2015. "Remaining useful life estimation under degradation and shock damage," Journal of Risk and Reliability, , vol. 229(3), pages 200-208, June.
    8. Zio, Enrico & Compare, Michele, 2013. "Evaluating maintenance policies by quantitative modeling and analysis," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 53-65.
    9. Fauriat, William & Zio, Enrico, 2020. "Optimization of an aperiodic sequential inspection and condition-based maintenance policy driven by value of information," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    10. Ji Hwan Cha & Maxim Finkelstein & Gregory Levitin, 2017. "Bivariate preventive maintenance for repairable systems subject to random shocks," Journal of Risk and Reliability, , vol. 231(6), pages 643-653, December.
    11. Yousefi, Nooshin & Coit, David W. & Song, Sanling & Feng, Qianmei, 2019. "Optimization of on-condition thresholds for a system of degrading components with competing dependent failure processes," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    12. Mahmood Shafiee & Maxim Finkelstein, 2015. "A proactive group maintenance policy for continuously monitored deteriorating systems: Application to offshore wind turbines," Journal of Risk and Reliability, , vol. 229(5), pages 373-384, October.
    13. Lee, Juseong & Mitici, Mihaela, 2022. "Multi-objective design of aircraft maintenance using Gaussian process learning and adaptive sampling," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    14. Caballé, N.C. & Castro, I.T. & Pérez, C.J. & Lanza-Gutiérrez, J.M., 2015. "A condition-based maintenance of a dependent degradation-threshold-shock model in a system with multiple degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 98-109.
    15. Dong, Wenjie & Liu, Sifeng & Bae, Suk Joo & Cao, Yingsai, 2021. "Reliability modelling for multi-component systems subject to stochastic deterioration and generalized cumulative shock damages," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    16. Cha, Ji Hwan & Finkelstein, Maxim & Levitin, Gregory, 2018. "Bivariate preventive maintenance of systems with lifetimes dependent on a random shock process," European Journal of Operational Research, Elsevier, vol. 266(1), pages 122-134.
    17. Shafiee, Mahmood & Finkelstein, Maxim, 2015. "An optimal age-based group maintenance policy for multi-unit degrading systems," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 230-238.
    18. Jingyi Zhao & Chunhai Gao & Tao Tang, 2022. "A Review of Sustainable Maintenance Strategies for Single Component and Multicomponent Equipment," Sustainability, MDPI, vol. 14(5), pages 1-22, March.
    19. Khac Tuan Huynh & Anne Barros & Christophe Bérenguer, 2012. "Adaptive condition-based maintenance decision framework for deteriorating systems operating under variable environment and uncertain condition monitoring," Journal of Risk and Reliability, , vol. 226(6), pages 602-623, December.
    20. Khac Tuan Huynh & Antoine Grall, 2020. "A condition-based maintenance model with past-dependent imperfect preventive repairs for continuously deteriorating systems," Journal of Risk and Reliability, , vol. 234(2), pages 333-358, April.

    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:103:y:2012:i:c:p:94-101. 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.