Current Practices for Preventive Maintenance and Expectations for Predictive Maintenance in East-Canadian Mines

Simon Robatto Simard, Michel Gamache, Philippe Doyon-Poulin

Article de revue (2023)

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Résumé

Preventive maintenance practices have been proven to reduce maintenance costs in many industries. In the mining industry, preventive maintenance is the main form of maintenance, especially for mobile equipment. With the increase of sensor data and the installation of wireless infrastructure within underground mines, predictive maintenance practices are beginning to be applied to the mining equipment maintenance process. However, for the transition from preventive to predictive maintenance to succeed, researchers must first understand the maintenance process implemented in mines. In this paper, we conducted interviews with 15 maintenance experts from 7 mining sites (6 gold, 1 diamond) across East-Canada to investigate the maintenance planning process currently implemented in Canadian mines. We documented experts’ feedback on the process, their expectations regarding the introduction of predictive maintenance in mining, and the usability of existing computerized maintenance management software (CMMS). From our results, we compiled a summary of actual maintenance practices and showed how they differ from theoretical practices. Finally, we list the Key Performance Indicators (KPIs) relevant for maintenance planning and user requirements to improve the usability of CMMS.

Mots clés

Organismes subventionnaires:
Fonds de recherche du Québec - Nature et technologies
Numéro de subvention:
2020-MN-281330
Adresse URL de PolyPublie:
Titre de la revue :
Mining ( vol. 3 , no 1 )
Maison d'édition:
MDPI
OAI:
oai:publications.polymtl.ca:10725
ORCID
Date du dépôt:
09 janv. 2023 15:44
Dernière modification:
14 oct. 2026 11:09
Citer en APA 7:
Robatto Simard, S., Gamache, M., & Doyon-Poulin, P. (2023). Current Practices for Preventive Maintenance and Expectations for Predictive Maintenance in East-Canadian Mines. Mining, 3(1), 26-53. https://doi.org/10.3390/mining3010002

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