IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v232y2018i2p140-150.html
   My bibliography  Save this article

A clustering approach for mining reliability big data for asset management

Author

Listed:
  • Francesco Cannarile
  • Michele Compare
  • Francesco Di Maio
  • Enrico Zio

Abstract

Big data from very large fleets of assets challenge the asset management, as the number of maintenance strategies to optimize and administrate may become very large. To address this issue, we exploit a clustering approach that identifies a small number of sets of assets with similar reliability behaviors. This enables addressing the maintenance strategy optimization issue once for all the assets belonging to the same cluster and, thus, introduces a strong simplification in the asset management. However, the clustering approach may lead to additional maintenance costs, due to the loss of refinement in the cluster reliability model. For this, we propose a cost model to support asset managers in trading off the simplification brought by the cluster-based approach against the related extra costs. The proposed approach is applied to a real case study concerning a set of more than 30,000 switch point machines.

Suggested Citation

  • Francesco Cannarile & Michele Compare & Francesco Di Maio & Enrico Zio, 2018. "A clustering approach for mining reliability big data for asset management," Journal of Risk and Reliability, , vol. 232(2), pages 140-150, April.
    • Handle: RePEc:sae:risrel:v:232:y:2018:i:2:p:140-150
    DOI: 10.1177/1748006X17716344
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X17716344
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X17716344?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. J. Gower & P. Legendre, 1986. "Metric and Euclidean properties of dissimilarity coefficients," Journal of Classification, Springer;The Classification Society, vol. 3(1), pages 5-48, March.
    2. Zio, Enrico & Compare, Michele, 2013. "Evaluating maintenance policies by quantitative modeling and analysis," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 53-65.
    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. Juan Izquierdo & Adolfo Crespo Márquez & Jone Uribetxebarria & Asier Erguido, 2019. "Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance," Energies, MDPI, vol. 12(11), pages 1-17, 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. 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.
    2. Guohuan Su & Adam Mertel & Sébastien Brosse & Justin M. Calabrese, 2023. "Species invasiveness and community invasibility of North American freshwater fish fauna revealed via trait-based analysis," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    3. Balepur, Prashant Narayan, 1998. "Impacts of Computer-Mediated Communication on Travel and Communication Patterns: The Davis Community Network Study," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6cb1f85c, Institute of Transportation Studies, UC Berkeley.
    4. Douglas L. Steinley & M. J. Brusco, 2019. "Using an Iterative Reallocation Partitioning Algorithm to Verify Test Multidimensionality," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 397-413, October.
    5. Michele Compare & Francesco Di Maio & Enrico Zio & Fausto Carlevaro & Sara Mattafirri, 2016. "Improving scheduled maintenance by missing data reconstruction: A double-loop Monte Carlo approach," Journal of Risk and Reliability, , vol. 230(5), pages 502-511, October.
    6. Anna Maria D’Arcangelis & Giulia Rotundo, 2016. "Complex Networks in Finance," Lecture Notes in Economics and Mathematical Systems, in: Pasquale Commendatore & Mariano Matilla-García & Luis M. Varela & Jose S. Cánovas (ed.), Complex Networks and Dynamics, pages 209-235, Springer.
    7. Carla Coltharp & Rene P Kessler & Jie Xiao, 2012. "Accurate Construction of Photoactivated Localization Microscopy (PALM) Images for Quantitative Measurements," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-15, December.
    8. Michele Compare & Paolo Marelli & Piero Baraldi & Enrico Zio, 2018. "A Markov decision process framework for optimal operation of monitored multi-state systems," Journal of Risk and Reliability, , vol. 232(6), pages 677-689, December.
    9. Lin, Boliang & Zhao, Yinan, 2021. "Synchronized optimization of EMU train assignment and second-level preventive maintenance scheduling," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    10. S. T. Buckland & Y. Yuan & E. Marcon, 2017. "Measuring temporal trends in biodiversity," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 461-474, October.
    11. Matthijs Warrens, 2008. "On the Indeterminacy of Resemblance Measures for Binary (Presence/Absence) Data," Journal of Classification, Springer;The Classification Society, vol. 25(1), pages 125-136, June.
    12. Stefano Bonnini & Getnet Melak Assegie & Kamila Trzcinska, 2024. "Review about the Permutation Approach in Hypothesis Testing," Mathematics, MDPI, vol. 12(17), pages 1-29, August.
    13. 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.
    14. Kivimäki, Ilkka & Shimbo, Masashi & Saerens, Marco, 2014. "Developments in the theory of randomized shortest paths with a comparison of graph node distances," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 600-616.
    15. Alfonso Gutierrez-Lopez & Carlos Chávez & Carlos Díaz-Delgado, 2022. "Autocorrelation Ratio as a Measure of Inertia for the Classification of Extreme Events," Mathematics, MDPI, vol. 10(12), pages 1-15, June.
    16. Raffaella Calabrese & Galina Andreeva & Jake Ansell, 2019. "“Birds of a Feather” Fail Together: Exploring the Nature of Dependency in SME Defaults," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 71-84, January.
    17. Jean-Baptiste Hasse, 2022. "Systemic risk: a network approach," Empirical Economics, Springer, vol. 63(1), pages 313-344, July.
    18. Heiberg, Jonas & Truffer, Bernhard & Binz, Christian, 2022. "Assessing transitions through socio-technical configuration analysis – a methodological framework and a case study in the water sector," Research Policy, Elsevier, vol. 51(1).
    19. Florian Schreiber, 2017. "Identification of customer groups in the German term life market: a benefit segmentation," Annals of Operations Research, Springer, vol. 254(1), pages 365-399, July.
    20. Joris Knoben & Leon A. G. Oerlemans & Annefleur R. Krijkamp & Keith G. Provan, 2018. "What Do They Know? The Antecedents of Information Accuracy Differentials in Interorganizational Networks," Organization Science, INFORMS, vol. 29(3), pages 471-488, June.

    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:232:y:2018:i:2:p:140-150. 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.