A Systematic Review of Remaining Useful Life Prediction in Roller Bearings Using Artificial Intelligence Techniques - Palaniappan, Rajkumar (Informatica, 2026)
Artificial Intelligence (AI) has demonstrated to be an effective method for predicting irregularities across various Industrial processes. In recent years, the development of predictive maintenance systems using AI techniques has attracted many researchers around the world. Maintenance planning has been effective with Remaining useful life prediction using AI in bearings. This review brings consideration to the role of AI in predicting the remaining useful life (RUL) of bearing components in various industrial processes. A systematic search was carried out in electronic databases such as Springer, IEEE, Elsevier, and the ACM Digital Library, with an emphasis on AI-based approaches for bearing RUL prediction. A brief summary of previous works is presented to show the development of technological advancement in this field of RUL prediction of roller bearing. Specifically, this review examines the types of bearing components studied, the sample sizes used for training AI models, the signal processing method and classification algorithm applied, and the outcomes achieved. The outcome of this review shows that hybrid approaches and deep learning models achieve better performance in predicting RUL in roller bearings. Finally, the review finds existing research gaps and provides recommendations for future improvements, aiming to guide future researchers toward more accurate and reliable RUL prediction models for bearings.