 A novel method for maintenance record clustering and its application to a case study of maintenance optimizationZhe Yang, Piero Baraldi and Enrico Zio Reliability Engineering and System Safety, 2020, vol. 203, issue C Abstract: Maintenance records contain information about equipment degradation, which is typically not used as “data†for maintenance modeling and optimization given the difficulty of analyzing text to extract relevant and usable information. In this work, we present a method for using such textual information by: 1) the identification of equipment degradation states through clustering the maintenance records using a Convolutional Neural Network (CNN) and 2) the development of a stochastic multi-state degradation model based on the information therein. The method is applied to a database of maintenance records collected from excavator buckets used for mining. The results show that the proposed CNN-based clustering method identifies homogeneous clusters of maintenance records representing different equipment degradation states and that the proposed stochastic multi-state degradation model based on this information can be used for improving maintenance. Keywords: Maintenance record; Text clustering; Convolutional neural network; Stochastic multi-state model; Maintenance optimization; Mining; Excavator bucket (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:203:y:2020:i:c:s0951832020306049 DOI: 10.1016/j.ress.2020.107103 Access Statistics for this article Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares More articles in Reliability Engineering and System Safety from Elsevier |
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