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 key contribution of this research lies in providing a structured synthesis of the PdM literature within Industry 4.0 using a PRISMA-guided review protocol and a set of clearly defined research questions, enabling a consistent analysis of the technical and organizational challenges associated with PdM implementation. 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).
Abdelhafid et al. (Wed,) studied this question.