Res-TCEANet: An expansive attention mechanism with positional correspondence based on semi-supervised temporal convolutional network for RUL estimation

•A semi-supervised framework based on Res-TCEANet is proposed to achieve accurate RUL prediction under the sparsely labeled data and noise.•A TCEANet is presented to learn a coherent feature representation and enhance the model’s integration ability to features.•An expansive attention mechanism is proposed to consider the links between feature-to-feature importance with relative positional correspondence.•The residual denoising module and TCEANet is presented to learn the difference between noisy and clean data for signal denoising. The accurate prediction of Remaining Useful Life (RUL) is crucial for various applications, and the relative position of time steps between features plays a significant role in this process. However, traditional deep learning models often struggle with extracting and positional corresponding information in temporal features, especially in the presence of noise and limited labeled data. To overcome these challenges, we propose a novel semi-supervised Residual-denoising Temporal Convolutional Expansive Attention Network (Res-TCEANet). This approach introduces a unique expansive attention mechanism (EAM) that enhances the modeling of long-term dependencies by addressing the positional correspondence of features across layers. The proposed EAM distinguishes itself from existing attention mechanisms by enabling TCEANet to model long sequences with a focus on positional coherence, resulting in more robust feature extraction. The Root Mean Square Error and Score of the proposed method on C-MAPSS dataset are 10.75, 12.27, 10.82, 12.33 and 114.82, 427.05, 161.96 746.93, respectively, which have demonstrated that our method achieves the start-of-the-art performance and outperforms other models.