An Adaptive BiGRU-ASSA-iTransformer Method for Remaining Useful Life Prediction of Bearing in Aerospace Manufacturing

In aerospace manufacturing, the reliability of machining equipment, particularly spindle bearings, is critical to maintaining productivity, as bearing health significantly constrains operational efficiency. Accurate prediction of the remaining useful life (RUL) of bearings can preempt failures, reduce downtime, and boost productivity. While conventional BiGRU-based models for bearing RUL prediction have shown promise, they often overlook handcrafted extracted time-series features that could enhance accuracy. This study introduces a novel model, BiGRU-ASSA-iTransformer, that integrates deep learning and handcrafted feature extraction to improve RUL prediction. The approach employs two parallel processes with a fusion step: First, a bi-directional gated recurrent unit (BiGRU) captures dynamic degradation features from raw vibration signals, with an adaptive sparse self-attention (ASSA) mechanism emphasizing short-term degradation cues. Second, 13 time-domain, frequency-domain, and statistical features, derived from traditional expertise, are processed using iTransformer to encode temporal correlations. These outputs are then fused via an attention mechanism. Experiments on the PHM 2012 and XJTU-SY datasets demonstrate that this model achieves the lowest prediction error and highest accuracy compared to existing methods, highlighting the value of combining handcrafted and deep learning approaches for robust RUL prediction in aerospace applications.