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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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
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    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.

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