IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v95y2010i7p786-796.html
   My bibliography  Save this article

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 for ScienceDirect subscribers only
    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 search for a different version of it.

    References listed on IDEAS

    as
    1. Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
    2. 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.
    3. Adolfo Crespo Márquez, 2007. "The Maintenance Management Framework," Springer Series in Reliability Engineering, Springer, number 978-1-84628-821-0, March.
    4. 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.
    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. Singh, Gurmeet & Anil Kumar, T.Ch. & Naikan, V.N.A., 2019. "Efficiency monitoring as a strategy for cost effective maintenance of induction motors for minimizing carbon emission and energy consumption," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 193-201.
    2. Niu, Gang & Jiang, Junjie, 2017. "Prognostic control-enhanced maintenance optimization for multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 218-226.
    3. Quintanilha, Igor M. & Elias, Vitor R.M. & da Silva, Felipe B. & Fonini, Pedro A.M. & da Silva, Eduardo A.B. & Netto, Sergio L. & Apolinário, José A. & de Campos, Marcello L.R. & Martins, Wallace A., 2021. "A fault detector/classifier for closed-ring power generators using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    4. He, Rui & Tian, Zhigang & Wang, Yifei & Zuo, Mingjian & Guo, Ziwei, 2023. "Condition-based maintenance optimization for multi-component systems considering prognostic information and degraded working efficiency," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Afzali, Peyman & Keynia, Farshid & Rashidinejad, Masoud, 2019. "A new model for reliability-centered maintenance prioritisation of distribution feeders," Energy, Elsevier, vol. 171(C), pages 701-709.
    6. Vega, Manuel A. & Hu, Zhen & Fillmore, Travis B. & Smith, Matthew D. & Todd, Michael D., 2021. "A Novel Framework for Integration of Abstracted Inspection Data and Structural Health Monitoring for Damage Prognosis of Miter Gates," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    7. Zhou, Dengji & Yu, Ziqiang & Zhang, Huisheng & Weng, Shilie, 2016. "A novel grey prognostic model based on Markov process and grey incidence analysis for energy conversion equipment degradation," Energy, Elsevier, vol. 109(C), pages 420-429.
    8. Chien, Yu-Hung & Sheu, Shey-Huei & Zhang, Zhe George, 2012. "Optimal maintenance policy for a system subject to damage in a discrete time process," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 1-10.
    9. Cha, Guesik & Park, Junseok & Moon, Ilkyeong, 2023. "Military aircraft flight and maintenance planning model considering heterogeneous maintenance tasks," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    10. Navneet Singh Bhangu & G. L. Pahuja & Rupinder Singh, 2017. "Enhancing reliability of thermal power plant by implementing RCM policy and developing reliability prediction model: a case study," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1923-1936, November.
    11. Arcos Jiménez, Alfredo & Gómez Muñoz, Carlos Quiterio & García Márquez, Fausto Pedro, 2019. "Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 2-12.
    12. Daneshkhah, A. & Stocks, N.G. & Jeffrey, P., 2017. "Probabilistic sensitivity analysis of optimised preventive maintenance strategies for deteriorating infrastructure assets," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 33-45.
    13. Adherbal Caminada Netto & Arthur Henrique de Andrade Melani & Carlos Alberto Murad & Miguel Angelo de Carvalho Michalski & Gilberto Francisco Martha de Souza & Silvio Ikuyo Nabeta, 2020. "A Novel Approach to Defining Maintenance Significant Items: A Hydro Generator Case Study," Energies, MDPI, vol. 13(23), pages 1-20, November.
    14. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.
    15. Tang, Yang & Liu, Qingyou & Jing, Jiajia & Yang, Yan & Zou, Zhengwei, 2017. "A framework for identification of maintenance significant items in reliability centered maintenance," Energy, Elsevier, vol. 118(C), pages 1295-1303.
    16. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
    17. Dashti, Reza & Yousefi, Shaghayegh, 2013. "Reliability based asset assessment in electrical distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 129-136.
    18. Zhao, Xufeng & Qian, Cunhua & Nakagawa, Toshio, 2013. "Optimal policies for cumulative damage models with maintenance last and first," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 50-59.
    19. Adolfo Crespo Márquez & Antonio de la Fuente Carmona & Sara Antomarioni, 2019. "A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency," Energies, MDPI, vol. 12(18), pages 1-25, September.
    20. Tinga, Tiedo, 2010. "Application of physical failure models to enable usage and load based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 95(10), pages 1061-1075.
    21. Ramsey Jadim & Mirka Kans & Mohammed Alhattab & May Alhendi, 2021. "A Novel Condition Monitoring Procedure for Early Detection of Copper Corrosion Problems in Oil-Filled Electrical Transformers," Energies, MDPI, vol. 14(14), pages 1-12, July.
    22. Azadeh, A. & Asadzadeh, S.M. & Salehi, N. & Firoozi, M., 2015. "Condition-based maintenance effectiveness for series–parallel power generation system—A combined Markovian simulation model," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 357-368.
    23. Arthur H.A. Melani & Carlos A. Murad & Adherbal Caminada Netto & Gilberto F.M. Souza & Silvio I. Nabeta, 2019. "Maintenance Strategy Optimization of a Coal-Fired Power Plant Cooling Tower through Generalized Stochastic Petri Nets," Energies, MDPI, vol. 12(10), pages 1-28, May.

    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. 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.
    12. 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.
    13. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    14. 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).
    15. 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).
    16. 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).
    17. 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.
    18. 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.
    19. 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.
    20. Agnieszka Blokus & Przemysław Dziula, 2021. "Relations of Imperfect Repairs to Critical Infrastructure Maintenance Costs," Sustainability, MDPI, vol. 13(9), pages 1-19, 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: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.