A comprehensive overview of remaining useful life prediction: From traditional literature review to scientometric analysis

The increasing complexity of industrial systems has heightened the need for precise Remaining Useful Life (RUL) predictions. This paper provides a comprehensive overview of RUL prediction methods, processes, datasets, and tools, alongside insights from scientometric analysis. We categorize RUL prediction methods into model-based, data-driven, and hybrid methods, detailing key models and their applications. Model-based methods utilize physical failure mechanisms, enhancing interpretability, but with limited adaptability for complex systems. Data-driven methods use machine learning to extensive datasets, boosting adaptability but often at the cost of explainability. Hybrid methods aim to strike a balance between these strengths, offering both accuracy and flexibility across various applications. In addition, we outline the RUL prediction process, from data acquisition to implementation, and introduce the primary datasets used in studies of engines, bearings, and batteries as well as commonly used programming frameworks and open-source libraries. We also performed a scientometric analysis of 3442 articles from the Web of Science database, examining research areas, prominent research groups, and emerging trends. Our findings offer a comprehensive overview of the RUL prediction field, outlining its current trends and highlighting potential directions for future research.