Artificial Intelligence (AI) has emerged as a transformative force in the realm of industrial predictive maintenance (PdM), enabling businesses to anticipate equipment failures and optimize maintenance schedules. This paper examines the integration of AI technologies such as machine learning, deep learning, and IoT systems into predictive maintenance frameworks. Drawing on case studies and current implementations across various sectors, including manufacturing, steel production, and ICT infrastructure, the research examines key processes such as data acquisition, preprocessing, model development, and decision-making. It also evaluates the use of advanced sensors and wireless networks, the architecture of predictive models, and practical implementation strategies. Benefits, including reduced operational costs, minimized downtime, and enhanced safety, are discussed alongside challenges such as data quality, model accuracy, and organizational maturity. Ultimately, this paper underscores AI’s pivotal role in advancing Industry 4.0 by facilitating data-driven maintenance that is both intelligent and proactive. Keywords: Predictive Maintenance (PdM), Artificial Intelligence (AI), Industry 4.0, Machine Learning (ML), Internet of Things (IoT), Deep Learning, Remaining Useful Life (RUL), Condition Monitoring.
Andrew Emmanuel (Tue,) studied this question.