Predictive Maintenance in Industrial Automation and Smart Manufacturing Applications using Artificial Intelligence

Abstract

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.