Projet de Fin d'Études : Mémoire de Master

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Predictive maintenance: state of the art based on Machine learning methods
(ESTIN, 2024) BELAYALI Rezkia; OURARI Tinhinane
This study explores the evolving role of machine learning in revolutionizing predictive maintenance (PdM) within Industry 4.0, emphasizing the transition from traditional methods to advanced, data-driven approaches, particularly highlighting deep learning's transformative impact. It examines key technologies such as IoT (Internet of Things) sensors for real-time vibration analysis and addresses the efficacy of data-driven models, stressing the importance of managing data quality. The study also explores state-of-the-art approaches that integrate both single-model and multi-model frameworks, combining machine learning (ML) with physics-based models and statistical techniques. This integrated approach enhances anomaly detection, fault classification, and estimation of remaining useful life (RUL), contributing to a robust PdM framework designed for Industry 4.0 environments. Keywords: Predictive maintenance, industry 4.0, machine learning, single model, multimodel, data-driven, IoT, vibration, RUL, anomaly detection, classification.