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A novel method for maintenance record clustering and its application to a case study of maintenance optimization

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  • Yang, Zhe
  • Baraldi, Piero
  • Zio, Enrico

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.

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  • Yang, Zhe & Baraldi, Piero & Zio, Enrico, 2020. "A novel method for maintenance record clustering and its application to a case study of maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:reensy:v:203:y:2020:i:c:s0951832020306049
    DOI: 10.1016/j.ress.2020.107103
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    1. Li, Ying Yi & Chen, Ying & Yuan, Zeng Hui & Tang, Ning & Kang, Rui, 2017. "Reliability analysis of multi-state systems subject to failure mechanism dependence based on a combination method," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 109-123.
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    4. Chen, Yujie & Cowling, Peter & Polack, Fiona & Remde, Stephen & Mourdjis, Philip, 2017. "Dynamic optimisation of preventative and corrective maintenance schedules for a large scale urban drainage system," European Journal of Operational Research, Elsevier, vol. 257(2), pages 494-510.
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    8. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    9. Michael Stewart & Melinda Hodkiewicz & Wei Liu & Tim French, 2024. "MWO2KG and Echidna: Constructing and exploring knowledge graphs from maintenance data," Journal of Risk and Reliability, , vol. 238(5), pages 920-932, October.
    10. Rose, Rodrigo L. & Puranik, Tejas G. & Mavris, Dimitri N. & Rao, Arjun H., 2022. "Application of structural topic modeling to aviation safety data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    11. Yang, Ningning & Wang, Zhijian & Cai, Wenan & Li, Yanfeng, 2023. "Data Regeneration Based on Multiple Degradation Processes for Remaining Useful Life Estimation," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    12. Paulina Gackowiec & Edyta Brzychczy & Marek Kęsek, 2021. "Enhancement of Machinery Activity Recognition in a Mining Environment with GPS Data," Energies, MDPI, vol. 14(12), pages 1-19, June.
    13. Bhardwaj, Abhijeet Sandeep & Veeramani, Dharmaraj, 2025. "An unsupervised multi-class ensemble classifier for identifying equipment failure mechanisms from maintenance records," Reliability Engineering and System Safety, Elsevier, vol. 264(PB).
    14. Xiaoxue Ma & He Lan & Weiliang Qiao & Bing Han & Heilong He, 2024. "On the causation correlation of maritime accidents based on data mining techniques," Journal of Risk and Reliability, , vol. 238(5), pages 905-919, October.
    15. Valcamonico, Dario & Baraldi, Piero & Macêdo, July Bias & Moura, Márcio Das Chagas & Brown, Jonathan & Gauthier, Stéphane & Zio, Enrico, 2025. "A systematic procedure for the analysis of maintenance reports based on a taxonomy and BERT attention mechanism," Reliability Engineering and System Safety, Elsevier, vol. 257(PA).

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