Advancements in bearing health monitoring and remaining useful life prediction: techniques, challenges, and future directions

Abstract This paper presents a comprehensive review of recent advancements in bearing health monitoring and remaining useful life (RUL) prediction. It highlights key innovations in anomaly detection, health indicator construction, degradation modeling, and RUL estimation, examining developments across statistical, machine learning, and deep learning approaches while analyzing their strengths, limitations, and application contexts. Special emphasis is placed on the role of deep learning in capturing complex degradation patterns from multi-dimensional time series data and improving predictive accuracy in dynamic industrial settings. Additionally, this review explores multi-source data fusion techniques, which enhance anomaly detection robustness by integrating information from diverse sensor modalities. By identifying critical challenges and suggesting future research directions, this study aims to advance the development of robust and adaptive prediction models for intelligent maintenance in industrial applications.