A Pre-Activated Residual Parallel Convolutional Block-Based BiGRU Model for Remaining Useful Life Prediction

The accurate prediction of the Remaining Useful Life (RUL) of key mechanical equipment in modern industry is crucial for reducing production risks and optimizing maintenance decisions. However, existing Convolutional Neural Network (CNN)-based models lack an inherent memory mechanism, and single convolutional kernel-based CNN models fail to capture multi-scale temporal features effectively. Moreover, some existing methods fail to account for the stability of the model training process, which tends to result in prolonged training time and an elevated risk of overfitting. To overcome these problems, a pre-activated residual parallel convolutional block-based BiGRU model (PRPC-BiGRU) is proposed in this study. First, the residual parallel convolutional block (RPCB) is constructed to simultaneously extract multi-scale temporal features. Subsequently, the pre-activated convolutional structure, which applies normalization and activation function prior to convolution operations, is utilized to improve gradient propagation and training stability. Finally, experimental results using the aero-engine benchmark datasets to verify the effectiveness and superior prediction performance of the proposed PRPC-BiGRU model.