Intelligent Predictive Maintenance through Machine Learning in Industry 4.0 Environments: A Review of Methods and Applications
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I3P115Keywords:
Industry 4.0, Predictive Maintenance, Cyber-Physical Systems (CPS), Cloud Computing, Machine Learning, Prognostics and Health Management (PHM)Abstract
Rapid advancements in industrial automation and digital transformation have underscored the significance of intelligent predictive maintenance (Pd.M.) in ensuring the durability, efficacy, and reliability of industrial assets. It is common for reactive and preventative maintenance procedures to lead to expensive downtime, wasteful use of resources, and unforeseen breakdowns. As an alternative, Pd.M. anticipates equipment failures using machine learning techniques, sensor fusion, and real-time data analytics, which minimizes operational disturbances and permits proactive decision-making. This paper offers a thorough analysis of ML-driven Pd.M., highlighting significant methodologies for industrial equipment health assessment, anomaly prediction, and Fault detection using deep learning, supervised learning, reinforcement learning, and unsupervised learning. Furthermore, the research delves into the most significant obstacles related to cybersecurity, scalability, feature selection, data quality, model interpretability, and Eliot system integration. The impact of ML-driven Pd.M. on industrial maintenance, including its ability to optimize asset performance, lower upkeep expenses, and boost operational effectiveness, is demonstrated through a thorough examination of real-world case studies and developing trends. The findings underscore the transformative impact of intelligent maintenance solutions and emphasize the need for continuous advancements in ML techniques to further enhance predictive capabilities in complex industrial environments.
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