IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v95y2010i7p786-796.html

Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance

Author

Listed:
  • Niu, Gang
  • Yang, Bo-Suk
  • Pecht, Michael

Abstract

Maintenance has gained in importance as a support function for ensuring equipment availability, quality products, on-time deliveries, and plant safety. Cost-effectiveness and accuracy are two basic criteria for good maintenance. Reducing maintenance cost can increase enterprise profit, while accurate maintenance action can sustain continuous and reliable operation of equipment. As instrumentation and information systems become cheaper and more reliable, condition-based maintenance becomes an important tool for running a plant or a factory. This paper presents a novel condition-based maintenance system that uses reliability-centered maintenance mechanism to optimize maintenance cost, and employs data fusion strategy for improving condition monitoring, health assessment, and prognostics. The proposed system is demonstrated by way of reasoning and case studies. The results show that optimized maintenance performance can be obtained with good generality.

Suggested Citation

  • Niu, Gang & Yang, Bo-Suk & Pecht, Michael, 2010. "Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 786-796.
  • Handle: RePEc:eee:reensy:v:95:y:2010:i:7:p:786-796
    DOI: 10.1016/j.ress.2010.02.016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832010000591
    Download Restriction: Full text may be restricted for ScienceDirect subscribers

    File URL: https://libkey.io/10.1016/j.ress.2010.02.016?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Adolfo Crespo Márquez, 2007. "The Maintenance Management Framework," Springer Series in Reliability Engineering, Springer, number 978-1-84628-821-0, April.
    2. Dong, Ming & He, David, 2007. "Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis," European Journal of Operational Research, Elsevier, vol. 178(3), pages 858-878, May.
    3. Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
    4. Carmignani, Gionata, 2009. "An integrated structural framework to cost-based FMECA: The priority-cost FMECA," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 861-871.
    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. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    2. Asadzadeh, S.M. & Azadeh, A., 2014. "An integrated systemic model for optimization of condition-based maintenance with human error," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 117-131.
    3. Rajkumar Bhimgonda Patil & Basavraj S Kothavale & Laxman Yadu Waghmode, 2019. "Selection of time-to-failure model for computerized numerical control turning center based on the assessment of trends in maintenance data," Journal of Risk and Reliability, , vol. 233(2), pages 105-117, April.
    4. Qinming Liu & Daigao Li & Wenyi Liu & Tangbin Xia & Jiaxiang Li, 2021. "A Novel Health Prognosis Method for a Power System Based on a High-Order Hidden Semi-Markov Model," Energies, MDPI, vol. 14(24), pages 1-19, December.
    5. Sorce, A. & Greco, A. & Magistri, L. & Costamagna, P., 2014. "FDI oriented modeling of an experimental SOFC system, model validation and simulation of faulty states," Applied Energy, Elsevier, vol. 136(C), pages 894-908.
    6. 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.
    7. Rodrigo Andrade & Somayeh Moazeni & Jose Emmanuel Ramirez‐Marquez, 2020. "A systems perspective on contact centers and customer service reliability modeling," Systems Engineering, John Wiley & Sons, vol. 23(2), pages 221-236, March.
    8. Phan, Hieu Chi & Dhar, Ashutosh Sutra & Bui, Nang Duc, 2023. "Reliability assessment of pipelines crossing strike-slip faults considering modeling uncertainties using ANN models," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    9. Rocchetta, Roberto & Crespo, Luis G., 2021. "A scenario optimization approach to reliability-based and risk-based design: Soft-constrained modulation of failure probability bounds," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    10. Teng, Kuei-Yung & Thekdi, Shital A. & Lambert, James H., 2012. "Identification and evaluation of priorities in the business process of a risk or safety organization," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 74-86.
    11. Jiang, Hongrui & Ding, Long & Ji, Jie & Zhu, Jiping, 2024. "Building reliability of risk assessment of domino effects in chemical tank farm through an improved uncertainty analysis method," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    12. Ibsen Chivatá Cárdenas & Saad S.H. Al‐Jibouri & Johannes I.M. Halman & Frits A. van Tol, 2014. "Modeling Risk‐Related Knowledge in Tunneling Projects," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 323-339, February.
    13. García Nieto, P.J. & García-Gonzalo, E. & Sánchez Lasheras, F. & de Cos Juez, F.J., 2015. "Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 219-231.
    14. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    15. Salomon, Julian & Winnewisser, Niklas & Wei, Pengfei & Broggi, Matteo & Beer, Michael, 2021. "Efficient reliability analysis of complex systems in consideration of imprecision," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    16. Kaya, Gulsum Kubra & Hocaoglu, Mehmet Fatih, 2020. "Semi-quantitative application to the Functional Resonance Analysis Method for supporting safety management in a complex health-care process," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    17. Senderov, Sergey M. & Smirnova, Elena M. & Vorobev, Sergey V., 2020. "Analysis of vulnerability of fuel supply systems in gas-consuming regions due to failure of critical gas industry facilities," Energy, Elsevier, vol. 212(C).
    18. Zhou, Zhi-Jie & Hu, Chang-Hua & Xu, Dong-Ling & Chen, Mao-Yin & Zhou, Dong-Hua, 2010. "A model for real-time failure prognosis based on hidden Markov model and belief rule base," European Journal of Operational Research, Elsevier, vol. 207(1), pages 269-283, November.
    19. Zheng, Weimin & Huang, Xiaoting & Li, Yuan, 2017. "Understanding the tourist mobility using GPS: Where is the next place?," Tourism Management, Elsevier, vol. 59(C), pages 267-280.
    20. Hu, Bin & Seiler, Peter, 2015. "Pivotal decomposition for reliability analysis of fault tolerant control systems on unmanned aerial vehicles," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 130-141.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:95:y:2010:i:7:p:786-796. 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.