IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v136y2015icp120-131.html
   My bibliography  Save this article

Supervision and prognosis architecture based on dynamical classification method for the predictive maintenance of dynamical evolving systems

Author

Listed:
  • Traore, M.
  • Chammas, A.
  • Duviella, E.

Abstract

In this paper, we are concerned by the improvement of the safety, availability and reliability of dynamical systems’ components subjected to slow degradations (slow drifts). We propose an architecture for efficient Predictive Maintenance (PM) according to the real time estimate of the future state of the components. The architecture is built on supervision and prognosis tools. The prognosis method is based on an appropriated supervision technique that consists in drift tracking of the dynamical systems using AUDyC (AUto-adaptive and Dynamical Clustering), that is an auto-adaptive dynamical classifier. Thus, due to the complexity and the dynamical of the considered systems, the Failure Mode Effect and Criticity Analysis (FMECA) is used to identify the key components of the systems. A component is defined as an element of the system that can be impacted by only one failure. A failure of a key component causes a long downtime of the system. From the FMECA, a Fault Tree Analysis (FTA) of the system are built to determine the propagation laws of a failure on the system by using a deductive method. The proposed architecture is implemented for the PM of a thermoregulator. The application on this real system highlights the interests and the performances of the proposed architecture.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:136:y:2015:i:c:p:120-131
    DOI: 10.1016/j.ress.2014.12.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832014003123
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2014.12.005?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. Muller, Alexandre & Suhner, Marie-Christine & Iung, Benoît, 2008. "Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system," Reliability Engineering and System Safety, Elsevier, vol. 93(2), pages 234-253.
    2. Pinjala, Srinivas Kumar & Pintelon, Liliane & Vereecke, Ann, 2006. "An empirical investigation on the relationship between business and maintenance strategies," International Journal of Production Economics, Elsevier, vol. 104(1), pages 214-229, November.
    3. Elsayed, E.A. & Zhang, Hao, 2007. "Design of PH-based accelerated life testing plans under multiple-stress-type," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 286-292.
    4. Swanson, Laura, 2001. "Linking maintenance strategies to performance," International Journal of Production Economics, Elsevier, vol. 70(3), pages 237-244, April.
    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. 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.
    7. Martorell, S. & Carlos, S. & Villanueva, J.F. & Sanchez, A.I & Galvan, B. & Salazar, D. & Cepin, M., 2006. "Use of multiple objective evolutionary algorithms in optimizing surveillance requirements," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 1027-1038.
    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. Ahmed, Umair & Carpitella, Silvia & Certa, Antonella, 2021. "An integrated methodological approach for optimising complex systems subjected to predictive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Zhang, Liangwei & Lin, Jing & Karim, Ramin, 2015. "An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 482-497.
    3. Meng, Huixing & Liu, Xuan & Xing, Jinduo & Zio, Enrico, 2022. "A method for economic evaluation of predictive maintenance technologies by integrating system dynamics and evolutionary game modelling," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    4. Catelani, M. & Ciani, L. & Venzi, M., 2018. "Failure modes, mechanisms and effect analysis on temperature redundant sensor stage," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 425-433.
    5. Belkacem, Lobna & Simeu-Abazi, Zineb & Dhouibi, Hedi & Gascard, Eric & Messaoud, Hassani, 2017. "Diagnostic and prognostic of hybrid dynamic systems: Modeling and RUL evaluation for two maintenance policies," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 98-109.

    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. Muchiri, Peter & Pintelon, Liliane & Gelders, Ludo & Martin, Harry, 2011. "Development of maintenance function performance measurement framework and indicators," International Journal of Production Economics, Elsevier, vol. 131(1), pages 295-302, May.
    2. Janak Priyantha, 2021. "Literature Review: The Role of Organizational Factors in Maintenance Organizations Affecting Their Manufacturing Performance, From Sri Lankan Cultural Perspective," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 5(4), pages 353-366, April.
    3. Alsyouf, Imad, 2009. "Maintenance practices in Swedish industries: Survey results," International Journal of Production Economics, Elsevier, vol. 121(1), pages 212-223, September.
    4. Gary, Linnéusson & Amos, Ng H.C. & Tehseen, Aslam, 2018. "Towards strategic development of maintenance and its effects on production performance by using system dynamics in the automotive industry," International Journal of Production Economics, Elsevier, vol. 200(C), pages 151-169.
    5. Dowlatshahi, Shad, 2008. "The role of industrial maintenance in the maquiladora industry: An empirical analysis," International Journal of Production Economics, Elsevier, vol. 114(1), pages 298-307, July.
    6. 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.
    7. Elizaveta Gavrikova & Irina Volkova & Yegor Burda, 2020. "Strategic Aspects of Asset Management: An Overview of Current Research," Sustainability, MDPI, vol. 12(15), pages 1-31, July.
    8. 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.
    9. Özgür-Ünlüakın, Demet & Türkali, Busenur, 2021. "Evaluation of proactive maintenance policies on a stochastically dependent hidden multi-component system using DBNs," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    10. 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.
    11. Johannes Freiesleben & Nicolas Gu'erin, 2015. "Homogenization and Clustering as a Non-Statistical Methodology to Assess Multi-Parametrical Chain Problems," Papers 1505.03874, arXiv.org, revised Dec 2017.
    12. Alsyouf, Imad, 2007. "The role of maintenance in improving companies' productivity and profitability," International Journal of Production Economics, Elsevier, vol. 105(1), pages 70-78, January.
    13. Le Son, Khanh & Fouladirad, Mitra & Barros, Anne & Levrat, Eric & Iung, Benoît, 2013. "Remaining useful life estimation based on stochastic deterioration models: A comparative study," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 165-175.
    14. Faccio, M. & Persona, A. & Sgarbossa, F. & Zanin, G., 2014. "Industrial maintenance policy development: A quantitative framework," International Journal of Production Economics, Elsevier, vol. 147(PA), pages 85-93.
    15. Green, Maxwell H. & Davies, Philip & Ng, Irene C.L., 2017. "Two strands of servitization: A thematic analysis of traditional and customer co-created servitization and future research directions," International Journal of Production Economics, Elsevier, vol. 192(C), pages 40-53.
    16. Bokrantz, Jon & Skoogh, Anders & Berlin, Cecilia & Wuest, Thorsten & Stahre, Johan, 2020. "Smart Maintenance: a research agenda for industrial maintenance management," International Journal of Production Economics, Elsevier, vol. 224(C).
    17. Toon Vanderschueren & Robert Boute & Tim Verdonck & Bart Baesens & Wouter Verbeke, 2022. "Prescriptive maintenance with causal machine learning," Papers 2206.01562, arXiv.org.
    18. 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.
    19. Briš, Radim & Byczanski, Petr, 2013. "Effective computing algorithm for maintenance optimization of highly reliable systems," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 77-85.
    20. Vivek Warke & Satish Kumar & Arunkumar Bongale & Ketan Kotecha, 2021. "Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis," Sustainability, MDPI, vol. 13(18), pages 1-49, September.

    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:136:y:2015:i:c:p:120-131. 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.