The digital transformation of industrial systems, driven by Industry 4.0, has profoundly reshaped maintenance strategies. Traditional approaches (corrective and scheduled preventive maintenance) are now showing their limitations in the face of increasing equipment complexity, higher requirements for availability, safety, cost reduction, and sustainability. Predictive Maintenance (PdM) and Prognostics and Health Management (PHM) rely on smart sensors, the Industrial Internet of Things (IIoT), advanced data analytics, artificial intelligence, Machine Learning, and Deep Learning to anticipate failures, estimate the Remaining Useful Life (RUL), and optimize maintenance decision-making. However, despite significant scientific progress, several challenges remain: difficulties in large-scale industrial deployment, data heterogeneity and quality issues, integration of PdM/PHM models into existing systems (CMMS, ERP), lack of feedback from real-world implementations and standardized frameworks, and the gap between academic research and real industrial applications. The symposium “Maintenance 4.0: PdM – PHM” therefore aims to bring together the scientific and industrial communities to discuss the challenges, barriers, and perspectives of predictive maintenance and PHM in the context of Industry 4.0, while promoting innovative, reliable, and scalable solutions for industrial applications. Discipline scientifique :