The fast development of the Industry 4.0 system has turned the old fabrication system into a smart, connected ecosystem sharing the level of rapid evolution that requires new solutions to the efficiency of the business processes and even equipment reliability. Artificial Intelligence (AI) has allowed predictive maintenance (PdM) to develop into a strategic method of reducing unplanned downtimes, maximizing machine life, and minimizing maintenance expenses. This paper investigates the possibility of systems such as machine learning, deep learning, and data analytics, being integrated into the smart manufacturing environment, in order to predict equipment breakdowns before it breaks down. It is a thorough review of the state-of-the-art AI models used in PdM, a review of the effectiveness of these models using the real-time sensor data and a modular system to implement the AI models in different industrial environments. By means of comparing and contrasting classic and AI-enhanced maintenance systems, the given research underscores better performance of intelligent PdM in terms of optimal production processes and decision-making. The limitations of key issues including sparsity of data, scalability issues, and model explanation have been addressed, as well as how this research might move forward into the future through the use of edge computing, the use of digital twins, and explainable AI. The results highlight the transformational role of AI in establishing resilient, cost effective and sustainable manufacturing systems.
Hossan et al. (Sat,) studied this question.
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