IDEAS home Printed from https://ideas.repec.org/a/sae/globus/v17y2016i1p105-115.html
   My bibliography  Save this article

Development of Predictive Maintenance Model for N-Component Repairable System Using NHPP Models and System Availability Concept

Author

Listed:
  • Nishit Kumar Srivastava
  • Sandeep Mondal

Abstract

The technologically intensive nature of the predictive maintenance (PdM) method restricts its use to companies with higher turnover. This research is aimed to propose a PdM model for an N-component repairable system by integrating non-homogeneous Poisson process (NHPP) models and a system availability concept such that the use of technology is minimized, thereby extending its applicability to companies with lower turnover. It is known that manufacturing systems show reliability degradation with repeated overhauls and component replacements. This has the effect that the mean time between failures (MTBF) is non-identically distributed. Hence, the failure pattern of each component is analyzed using NHPP models and the mean system availability is calculated, which is now compared with the threshold system availability deciding the overall maintenance of the system. Further, the developed model is validated on a wheat flour mill.

Suggested Citation

  • Nishit Kumar Srivastava & Sandeep Mondal, 2016. "Development of Predictive Maintenance Model for N-Component Repairable System Using NHPP Models and System Availability Concept," Global Business Review, International Management Institute, vol. 17(1), pages 105-115, February.
    • Handle: RePEc:sae:globus:v:17:y:2016:i:1:p:105-115
    DOI: 10.1177/0972150915610697
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0972150915610697
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0972150915610697?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. Christer, A. H. & Wang, W. & Sharp, J. M., 1997. "A state space condition monitoring model for furnace erosion prediction and replacement," European Journal of Operational Research, Elsevier, vol. 101(1), pages 1-14, August.
    2. Bana e Costa, Carlos A. & Carnero, María Carmen & Oliveira, Mónica Duarte, 2012. "A multi-criteria model for auditing a Predictive Maintenance Programme," European Journal of Operational Research, Elsevier, vol. 217(2), pages 381-393.
    3. You, Ming-Yi & Li, Hongguang & Meng, Guang, 2011. "Control-limit preventive maintenance policies for components subject to imperfect preventive maintenance and variable operational conditions," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 590-598.
    4. Carnero Moya, M. Carmen, 2004. "The control of the setting up of a predictive maintenance programme using a system of indicators," Omega, Elsevier, vol. 32(1), pages 57-75, February.
    5. Carnero, MaCarmen, 2006. "An evaluation system of the setting up of predictive maintenance programmes," Reliability Engineering and System Safety, Elsevier, vol. 91(8), pages 945-963.
    6. Ming Tan, Cher & Raghavan, Nagarajan, 2008. "A framework to practical predictive maintenance modeling for multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1138-1150.
    7. Arash Shahin & Hadi Shirouyehzad & Ehsan Pourjavad, 2012. "Optimum maintenance strategy: a case study in the mining industry," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 12(3), pages 368-386.
    8. Chu, Chengbin & Proth, Jean-Marie & Wolff, Philippe, 1998. "Predictive maintenance: The one-unit replacement model," International Journal of Production Economics, Elsevier, vol. 54(3), pages 285-295, May.
    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. Curcurù, Giuseppe & Galante, Giacomo & Lombardo, Alberto, 2010. "A predictive maintenance policy with imperfect monitoring," Reliability Engineering and System Safety, Elsevier, vol. 95(9), pages 989-997.
    2. Traore, M. & Chammas, A. & Duviella, E., 2015. "Supervision and prognosis architecture based on dynamical classification method for the predictive maintenance of dynamical evolving systems," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 120-131.
    3. Pedregal, Diego J. & Carmen Carnero, Ma, 2006. "State space models for condition monitoring: a case study," Reliability Engineering and System Safety, Elsevier, vol. 91(2), pages 171-180.
    4. Carnero, MaCarmen, 2006. "An evaluation system of the setting up of predictive maintenance programmes," Reliability Engineering and System Safety, Elsevier, vol. 91(8), pages 945-963.
    5. Jaime Burgos & María Carmen Carnero, 2020. "Assessment of Social Responsibility in Education in Secondary Schools," Sustainability, MDPI, vol. 12(12), pages 1-38, June.
    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. Braglia, Marcello & Carmignani, Gionata & Frosolini, Marco & Zammori, Francesco, 2012. "Data classification and MTBF prediction with a multivariate analysis approach," Reliability Engineering and System Safety, Elsevier, vol. 97(1), pages 27-35.
    8. de Jonge, Bram & Teunter, Ruud & Tinga, Tiedo, 2017. "The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 21-30.
    9. Xia, Tangbin & Xi, Lifeng & Zhou, Xiaojun & Lee, Jay, 2012. "Dynamic maintenance decision-making for series–parallel manufacturing system based on MAM–MTW methodology," European Journal of Operational Research, Elsevier, vol. 221(1), pages 231-240.
    10. Jiawen Hu & Zuhua Jiang & Hong Wang, 2016. "Preventive maintenance for a single-machine system under variable operational conditions," Journal of Risk and Reliability, , vol. 230(4), pages 391-404, August.
    11. Ribeiro, M.A. & Silveira, J.L. & Qassim, R.Y., 2007. "Joint optimisation of maintenance and buffer size in a manufacturing system," European Journal of Operational Research, Elsevier, vol. 176(1), pages 405-413, January.
    12. Ferreira, Rodrigo J.P. & de Almeida, Adiel Teixeira & Cavalcante, Cristiano A.V., 2009. "A multi-criteria decision model to determine inspection intervals of condition monitoring based on delay time analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(5), pages 905-912.
    13. García, Fausto P. & Pedregal, Diego J. & Roberts, Clive, 2010. "Time series methods applied to failure prediction and detection," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 698-703.
    14. Mitra Fouladirad & Antoine Grall, 2015. "Monitoring and condition-based maintenance with abrupt change in a system’s deterioration rate," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(12), pages 2183-2194, September.
    15. Fernando A. F. Ferreira & Ieva Meidutė-Kavaliauskienė & Edmundas K. Zavadskas & Marjan S. Jalali & Sandra M. J. Catarino, 2019. "A Judgment-Based Risk Assessment Framework for Consumer Loans," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 7-33, January.
    16. Shafiee, Mahmood & Finkelstein, Maxim & Chukova, Stefanka, 2011. "On optimal upgrade level for used products under given cost structures," Reliability Engineering and System Safety, Elsevier, vol. 96(2), pages 286-291.
    17. Abrahamsen, Eirik Bjorheim & Milazzo, Maria Francesca & Selvik, Jon T. & Asche, Frank & Abrahamsen, HÃ¥kon Bjorheim, 2020. "Prioritising investments in safety measures in the chemical industry by using the Analytic Hierarchy Process," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    18. Yen-Luan Chen & Chin-Chih Chang & Dwan-Fang Sheu, 2016. "Optimum random and age replacement policies for customer-demand multi-state system reliability under imperfect maintenance," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(5), pages 1130-1141, April.
    19. Fausto Pedro García Márquez & Diego J. Pedregal & Clive Roberts, 2015. "New methods for the condition monitoring of level crossings," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(5), pages 878-884, April.
    20. Nguyen, Khanh T.P. & Medjaher, Kamal, 2019. "A new dynamic predictive maintenance framework using deep learning for failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 251-262.

    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:globus:v:17:y:2016:i:1:p:105-115. 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: http://www.imi.edu/ .

    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.