Artificial Intelligence and IoT-Based Predictive Maintenance: A Review of Intelligent Industrial Systems
Abstract
The integration of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) is fundamentally changing industrial maintenance from traditional reactive approaches toward intelligent predictive maintenance. IoT sensors continuously collect information related to temperature, vibration, pressure, energy consumption, and other operational parameters, while AI and ML techniques analyze these data to identify anomalies and predict potential equipment failures. This review systematically examines the development and application of AI- and IoT-based predictive maintenance across manufacturing, energy, transportation, and other industrial sectors. Different machine learning techniques, including decision trees, support vector machines, random forests, artificial neural networks, and deep learning models, are discussed in relation to fault detection, remaining useful life estimation, and failure prediction. The review also investigates the role of edge computing and cloud platforms in processing industrial sensor data. Particular attention is given to challenges involving data quality, imbalanced datasets, model explainability, cybersecurity, scalability, and real-time deployment. The potential of digital twins, federated learning, and explainable AI to improve predictive maintenance systems is also explored. Finally, future research directions are presented for developing reliable, autonomous, secure, and cost-effective industrial maintenance frameworks.
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