IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v238y2024i2p417-428.html
   My bibliography  Save this article

Optimizing inspection plan for corroded pipeline with considering imperfect maintenance

Author

Listed:
  • Yifei Wang
  • Chun Su
  • Mingjiang Xie

Abstract

Metal-loss corrosion may result in pipeline’s failure and expensive downtime loss. Thus, to keep the pipeline’s normal operation, it is crucial to draw up scientific inspection and maintenance plans. This study is to optimize the inspection plan for corroded pipeline. Limit state functions are established for the corrosion leakage and burst respectively, and the pipeline’s failure probability is obtained with Monte Carlo simulation. The pipeline’s failure probability is further evaluated by the copula function and with considering the correlation of different failure modes. Moreover, a hybrid failure rate model is developed to update the pipeline’s failure probability, where the age reduction factor and failure rate increase factor are adopted, and imperfect maintenance is taken into account. On this basis, an optimization model is established with the objective to minimize the total maintenance cost, and genetic algorithm is applied to optimize the inspection plans. A case study is conducted, and the optimal results are analyzed according to acceptable maximum failure probability characteristics for the areas with different risk levels. The results show that periodic inspection and perfect maintenance are suitable for high-risk areas, and the proposed inspection plan is more suitable for corroded pipeline in low or medium-risk areas.

Suggested Citation

  • Yifei Wang & Chun Su & Mingjiang Xie, 2024. "Optimizing inspection plan for corroded pipeline with considering imperfect maintenance," Journal of Risk and Reliability, , vol. 238(2), pages 417-428, April.
  • Handle: RePEc:sae:risrel:v:238:y:2024:i:2:p:417-428
    DOI: 10.1177/1748006X221136323
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X221136323
    Download Restriction: no

    File URL: https://libkey.io/10.1177/1748006X221136323?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
    ---><---

    References listed on IDEAS

    as
    1. A. Khatab, 2018. "Maintenance optimization in failure-prone systems under imperfect preventive maintenance," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 707-717, March.
    2. Liu, Gehui & Chen, Shaokuan & Jin, Hua & Liu, Shuang, 2021. "Optimum opportunistic maintenance schedule incorporating delay time theory with imperfect maintenance," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    Full references (including those not matched with items on IDEAS)

    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. Azizi, Fariba & Salari, Nooshin, 2023. "A novel condition-based maintenance framework for parallel manufacturing systems based on bivariate birth/birth–death processes," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    2. Zhu, Mixin & Zhou, Xiaojun, 2023. "Hybrid opportunistic maintenance policy for serial-parallel multi-station manufacturing systems with spare part overlap," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    3. Mosayebi Omshi, E. & Grall, A., 2021. "Replacement and imperfect repair of deteriorating system: Study of a CBM policy and impact of repair efficiency," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Liu, Gehui & Chen, Shaokuan & Ho, Tinkin & Ran, Xinchen & Mao, Baohua & Lan, Zhen, 2022. "Optimum opportunistic maintenance schedule over variable horizons considering multi-stage degradation and dynamic strategy," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    5. Xiaofeng Wang & Shu Guo & Jian Shen & Yang Liu, 2020. "Optimization of preventive maintenance for series manufacturing system by differential evolution algorithm," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 745-757, March.
    6. Ait Mokhtar, El Hassene & Laggoune, Radouane & Chateauneuf, Alaa, 2023. "Imperfect maintenance modeling and assessment of repairable multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    7. Xiaohui Chen & Lin Zhang & Ze Zhang, 2020. "An integrated model for maintenance policies and production scheduling based on immune–culture algorithm," Journal of Risk and Reliability, , vol. 234(5), pages 651-663, October.
    8. Li, Yaping & Xia, Tangbin & Chen, Zhen & Pan, Ershun, 2023. "Multiple degradation-driven preventive maintenance policy for serial-parallel multi-station manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. An, Youjun & Chen, Xiaohui & Hu, Jiawen & Zhang, Lin & Li, Yinghe & Jiang, Junwei, 2022. "Joint optimization of preventive maintenance and production rescheduling with new machine insertion and processing speed selection," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    10. Wang, Weikai & Chen, Xian, 2023. "Piecewise deterministic Markov process for condition-based imperfect maintenance models," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    11. Tangbin Xia & Xiangxin An & Huaqiang Yang & Yimin Jiang & Yuhui Xu & Meimei Zheng & Ershun Pan, 2023. "Efficient Energy Use in Manufacturing Systems—Modeling, Assessment, and Management Strategy," Energies, MDPI, vol. 16(3), pages 1-20, January.
    12. Zhu, Mixin & Zhou, Xiaojun, 2022. "Hypergraph-based joint optimization of spare part provision and maintenance scheduling for serial-parallel multi-station manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    13. Liu, Gehui & Chen, Shaokuan & Jin, Hua & Liu, Shuang, 2021. "Optimum opportunistic maintenance schedule incorporating delay time theory with imperfect maintenance," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    14. Juhyun Lee & Byunghoon Kim & Suneung Ahn, 2019. "Maintenance Optimization for Repairable Deteriorating Systems under Imperfect Preventive Maintenance," Mathematics, MDPI, vol. 7(8), pages 1-17, August.
    15. Christopher Hagedorn & Johannes Huegle & Rainer Schlosser, 2022. "Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2027-2043, October.
    16. Cheng, Guoqing & Li, Ling & Shangguan, Chunxia & Yang, Nan & Jiang, Bo & Tao, Ningrong, 2023. "Optimal joint inspection and mission abort policy for a partially observable system," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    17. Alotaibi, Naif M. & Scarf, Philip & Cavalcante, Cristiano A.V. & Lopes, Rodrigo S. & de Oliveira e Silva, André Luiz & Rodrigues, Augusto J.S. & Alyami, Salem A., 2023. "Modified-opportunistic inspection and the case of remote, groundwater well-heads," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    18. Wang, Siqi & Zhao, Xian & Wu, Congshan & Wang, Xiaoyue, 2023. "Joint optimization of multi-stage component reassignment and preventive maintenance for balanced systems considering imperfect maintenance," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    19. Youngju Kim & Hoyeop Lee & Chang Ouk Kim, 2023. "A variational autoencoder for a semiconductor fault detection model robust to process drift due to incomplete maintenance," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 529-540, February.
    20. Azimpoor, Samareh & Taghipour, Sharareh & Farmanesh, Babak & Sharifi, Mani, 2022. "Joint Planning of Production and Inspection of Parallel Machines with Two-phase of Failure," Reliability Engineering and System Safety, Elsevier, vol. 217(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:sae:risrel:v:238:y:2024:i:2:p:417-428. 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: SAGE Publications (email available below). General contact details of provider: .

    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.