Recently, with the advent of Industry 4.0 (I4.0), intelligent systems, Machine Learning (ML) within Artificial Intelligence (AI), and Predictive Maintenance (PM) methodologies have been widely implemented in industries to manage the health state of machinery in the workplace. Owing to the shift in technology regarding 14.0, information technologies, computerized control, and networked communications, it is feasible to amass extensive data on working and process circumstances generated by various equipment. This data can be utilized for automated fault identification and evaluation to reduce downtime, enhance component use rates, and prolong their remaining valuable lifespans. PM is essential to long-term smart manufacturing in I4.0. AI approaches have become a viable instrument in project management systems for intelligent production in I4.0, attracting significant interest from writers in recent years. This paper seeks to thoroughly review recent developments in AI techniques extensively utilized for predictive maintenance in smart manufacturing within I4.0. It categorizes the research based on AI methods, categories, the technology employed, collecting data devices, information organization, dimensions, and type. It emphasizes the principal contributions of investigators, thereby providing rules and a foundation for future studies.
Shetty et al. (Thu,) studied this question.
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