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Abstract
ABSTRACT
Maintenance engineering has taken up more and more a strategic function in recent decades due to technological advancements and its role in asset productivity. Over time, a plethora of methods have been proposed, shifting from reactive approaches to complex, data-driven strategies focused on failure prediction (e.g. through Machine learning) and knowledge management (e.g. based on ontologies and large language models). The advancements achieved by maintenance have also beneficially impacted production quality, sustainability, and safety. This work presents the results of a systematic literature review of papers published on the topic of maintenance in the past 30 years. In particular, natural language processing has been used to analyze abstract, extract topics and, through further analysis delineate past, current, and future trends in the field of maintenance engineering in manufacturing. This work contributes to define a vision on how maintenance in manufacturing will evolve in the next future.
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Details
1 Department of Management, Information and Production Engineering, University of Bergamo , Dalmine (BG) , Italy
2 Department of Industrial and Manufacturing Engineering, University of Malta Msida , Malta Msida , Malta
3 Department of Mechanical, Chemical, and Material Engineering, University of Cagliari , Cagliari , Italy