A clustering approach for mining reliability big data for asset management

Francesco Cannarile, Michele Compare, Francesco Di Maio and Enrico Zio

Journal of Risk and Reliability, 2018, vol. 232, issue 2, 140-150

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.

Keywords: Big Data; preventive maintenance; spectral clustering; fleet of assemblies; maintenance cost analysis (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:232:y:2018:i:2:p:140-150

DOI: 10.1177/1748006X17716344

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