An Unsupervised Data-Driven Framework for Bearing Failure Prognosis via Health Stage Clustering and Artificial Neural Network-Based Remaining Useful Life Estimation

Reliable bearing-failure prognosis in induction machines remains a critical research challenge, as it directly impacts system availability, maintenance efficiency, and overall operational safety. To address this challenge, it is essential to develop an online prognostic system capable of continuously assessing bearing health and predicting future failures in real time. This paper proposes a novel unsupervised data-driven prognostic framework for induction machine bearings that integrates advanced signal processing techniques for the preprocessing step, data clustering to construct bearing health stage (HS), artificial neural network (ANN) forecasting using a designed health indicator (HI) based on the latest historical observations, and a fine-tuning model to improve the estimation of remaining useful life (RUL) for induction machine bearings using vibration and temperature signals provided by the PRONOSTIA and NASA-IMS experimentation platform. The results show that the proposed approach is an effective way for bearing RUL estimation.