A Review on the Remaining Useful Life Prediction of Rotating Machinery Based on Deep Learning

Abstract Remaining useful life (RUL) prediction is a critical technology in prognostics and health management. Rotating machinery plays an indispensable role in industrial processes, and accurate RUL prediction for such equipment is essential to ensure production continuity, optimize maintenance strategies, and reduce operational costs. In recent years, deep learning has emerged as a powerful tool for the analysis of multisource monitoring data derived from rotating machinery, owing to its exceptional proficiency in feature extraction and nonlinear modeling. This approach can effectively excavate the deeper-level information within the data, thereby improving prediction accuracy. Consequently, deep learning demonstrates significant application value and broad development prospects in RUL prediction for rotating machinery. This article provides a comprehensive review of the application of deep learning techniques in RUL prediction for rotating machinery. First, it introduces a typical framework for RUL prediction based on deep learning methodologies. The article then elaborates on the theoretical foundations of several key learning methods, including autoencoders, convolutional neural networks, long short-term memory, gated recurrent units, as well as emerging models such as transformers, temporal convolutional networks, and graph neural networks. Subsequently, the article summarizes the applications of these deep learning methods in RUL prediction for rotating machinery, evaluating the specific problems that each method addresses based on their distinct characteristics. Finally, it discusses the significant challenges that deep learning faces within this domain and outlines potential directions for future research.