Multi-stage degradation feature with dynamic feedback mechanism for remaining useful life prediction

Bearings, essential in industrial rotating machinery, undergo progressive wear over time. This degradation occurs in multiple stages with varying rates, characteristic changes, and information content, presenting challenges for accurate remaining useful life (RUL) prediction. Existing methods do not account for the impact of multi-stage degradation characteristic differences on the prediction model, leading to significant prediction errors. This paper presents a novel dynamic feedback mechanism-driven approach based on multi-stage degradation characteristics. The method introduces a trend detection technique to segment monitoring data into distinct degradation stages. By continuously updating the parameters of the degradation model, the model accurately identifies transition points between stages. In a key innovation, a dynamic feedback mechanism is established between multi-stage features and multi-scale parameters, integrated with a multi-scale densely connected temporal convolutional network (TCN) prediction model. This mechanism enables adaptive adjustments to dilation rates and kernel sizes in response to feature variations across different stages, facilitating the precise extraction of stage-specific degradation patterns. Experimental validation using the XJTU-SY and SMU bearing datasets confirms that the proposed method outperforms existing approaches in terms of degradation feature extraction and multi-stage adaptive prediction, showcasing its practical applicability and superior performance in complex industrial settings..