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Adoptable approaches to predictive maintenance in mining industry: An overview

Author

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  • Dayo-Olupona, Oluwatobi
  • Genc, Bekir
  • Celik, Turgay
  • Bada, Samson

Abstract

The mining industry contributes to the expansion of the global economy by generating vital commodities. For continuous production, the industry relies significantly on machinery and equipment, which, as a result of greater modernization, are becoming increasingly complex, with a variety of systems and subsystems. However, maintaining the machinery and equipment used in the mining industry can be complex and costly. To improve the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies for equipment maintenance and to determine the best maintenance strategies, a systematic literature review was conducted to summarise the current state of research on equipment-related predictive maintenance (RP) in the mining industry. The review provides an overview of maintenance practices in the mining sector and examines PdM methodologies and processes used in other industries that may be applicable to the mining industry. In addition, this study discusses the different PdM architectures, processes, phases, and models (statistical and ML-based) used in creating a PdM plan. Furthermore, the review explores potential implementation directions for the PdM in the mining industry and highlights the challenges.

Suggested Citation

  • Dayo-Olupona, Oluwatobi & Genc, Bekir & Celik, Turgay & Bada, Samson, 2023. "Adoptable approaches to predictive maintenance in mining industry: An overview," Resources Policy, Elsevier, vol. 86(PA).
    • Handle: RePEc:eee:jrpoli:v:86:y:2023:i:pa:s0301420723010024
    DOI: 10.1016/j.resourpol.2023.104291
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