Prediction of residual life of rotating components based on adaptive dynamic weighting and gated double attention unit

Abstract

Gears and bearings play vital roles as essential transmission components in mechanical drivetrains. Accurately predicting the remaining useful life (RUL) of these components is paramount to ensure optimal performance and prevent unexpected failures. To enhance the precision of RUL prediction, a novel method has been developed which involves constructing health indicators (HI) and implementing an adaptive dynamic weighting (ADW) on a gated dual attention unit (GDAU). The process commences by extracting multi-dimensional time-frequency domain features from vibration signals, which are then refined using an improved kernel principal component analysis (Adaptive Kernel Principal Component Analysis – AKPCA) to extract key components. Subsequently, the constructed HI is fine-tuned through an optimization process utilizing the exponentially weighted moving average method. Finally, the ADW strategy dynamically adjusts the input weights of the HI, and the GDAU model is employed to predict the RUL of gears and bearings. Experiment and comparison results have validated the effectiveness and advantages of the proposed method.