A clustering approach for mining reliability big data for asset management. (April 2018)

• Record Type: Journal Article
• Authors: Cannarile, Francesco; Compare, Michele; Di Maio, Francesco; Zio, Enrico
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 232:Number 2(2018:Apr.)
• Page Start: 140
• Page End: 150
• Publication Date: 2018-04
• Subjects: Big Data -- preventive maintenance -- spectral clustering -- fleet of assemblies -- maintenance cost analysis; Reliability (Engineering) -- Mathematical models -- Periodiclals; Risk assessment -- Mathematical models -- Periodicals; Engineering design -- Mathematical models -- Periodicals; 620.00452
• DOI: 10.1177/1748006X17716344
• Languages: English
• ISSNs: 1748-006X

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.