Abstract In the context of the transition to Industry 4.0, Predictive Maintenance (PdM) emerges as a key strategy to anticipate failures, reduce operational costs, and optimize the availability of industrial assets. This study presents a systematic review of recent works focused on approaches, methods, and challenges related to PdM, with particular emphasis on the integration of Artificial Intelligence (AI), the Internet of Things (IoT), and Big Data.A distinctive contribution of this research lies in the development of a taxonomy of maintenance strategies, tracing the evolution from corrective and preventive approaches to predictive and prescriptive paradigms, thereby providing a structured framework for positioning PdM within Industry 4.0. In addition, the review is guided by a set of research questions formulated to better capture the stakes and challenges associated with PdM implementation at both the technical and organizational levels. The analysis classifies scientific contributions based on prediction models (physics-based, knowledge-based, data-driven, and hybrid), evaluates machine learning algorithms (Random Forest, SVM, deep neural networks, transformers, etc.), and identifies the main technical and industrial limitations. The findings reveal that despite technological advances, significant obstacles persist in real-time deployment, model robustness, heterogeneous data management, and cybersecurity. The article also outlines promising perspectives for future research, with particular attention to prescriptive maintenance, digital twins, and explainable artificial intelligence (XAI).
Alaoui et al. (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: