AdvancesinAI-EnabledPredictiveMaintenance:Models,Industrial Applications, and Research Frontiers

Abstract

Predictive Maintenance (PdM), a paradigm enabled by the integration of Artificial Intelligence (AI), the Industrial Internet of Things (IIoT),and advanced data analytics, is presently navigating a crucial shift in the industrial landscape from reactive to preventative maintenance tactics. This change makes it easier to switch from fail-and-fix approaches to predict-and-prevent tactics, which maximizes asset avail-ability, lowers operatingcosts, and improves safety. With a thorough technical examination of deep learning architectures such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Autoencoders, and the new Transformer models, this paper investigates the state-of-the-art in AI-enabled PdM. With particular focus on energy systems, thermal power generation, and rotating machinery—areas where thermodynamic efficiency and energy analysis are crucial—it critically investigates the implementation of these models across important sectors. Additionally, the study addresses ongoing issues with data scarcity, model interpretability, and cybersecurity in safety-critical situations by identify-ing and discussing emerging research horizons including Explainable AI (XAI), Digital Twins, and Phys-ics-Informed Machine Learning (PIML)