Prediction of the Remaining Life of Rolling Bearings Based on Health Indicators and Temporal Attention Networks

Accurate remaining useful life (RUL) prediction of rolling bearings was essential for condition-based maintenance because bearing service degradation was primarily governed by progressive rolling-contact fatigue at the rollingelement–raceway interface, whereas vibration signals provided measurable responses to this degradation rather than being its physical cause. However, reliable RUL prediction remained challenging because vibration measurements were noisy, nonlinear, stage-dependent, and sensitive to operating-condition shifts. In this study, a health-indicator-guided temporal-attention framework was developed for bearing RUL prediction using public run-to-failure vibration datasets. The novelty of this work lay in integrating degradation-consistent health indicator construction, sliding-window life-cycle representation, and HI-guided temporal attention into a unified and interpretable prediction framework. First, degradation-sensitive vibration features were extracted and fused into a compact health indicator (HI) to represent the progressive deterioration trend. Then, sliding-window sequences were generated and processed by a Transformer-based temporal-attention network, through which long-range temporal dependencies were captured and higher weights were assigned to informative degradation segments near stage transitions and late-life acceleration. Experiments on the XJTU-SY and IMS datasets showed that the proposed method improved prediction stability, reduced late-life error amplification, and achieved better performance than baseline variants without HI or temporal attention. Ablation analysis confirmed that HI construction mitigated cross-stage drift, whereas temporal attention enhanced transition sensitivity during accelerated degradation. Robustness and cross-domain tests further indicated that the method maintained acceptable degradation-following behavior under noise perturbations and operating-condition changes, although explicit domain-adaptation mechanisms were still required for strongly shifted target domains.