An adaptive model with dual-dimensional attention for remaining useful life prediction of aero-engine

• Published in: Knowledge-based systems 2024-06, Vol.293, p.111738, Article 111738
• Main Authors: Gan, Fanfan, Shao, Haidong, Xia, Baizhan
• Format: Article
• Language: English
• Subjects: Aero-engine; C-mapss dataset; Dual-dimensional attention; Remaining useful life estimation; Temporal convolutional network
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2024.111738

•We design an model for remaining useful life prediction of the aero-engine.•We develop a module to parallel extract the dual-dimensional attention features.•We propose an adaptive TCN model to extract the deep temporal representations.•We achieve a better prediction accuracy than the state-of-art methods. As an indispensable part in prognostics and health management (PHM) of mechanical systems, the remaining useful life (RUL) estimation has been studied widely and intensively. Recently, with the boom in deep learning, the traditional data-driven prognostic methods like the temporal convolutional neural networks (TCNs), have made remarkable progress in the RUL estimation. However, the conventional TCNs methods do not have a well-established mechanism to parallel weigh the dual-dimensional features of multi-sensory data. Moreover, the conventional TCNs have a fixed network structure which is not flexible to learn deep temporal representations. Here, we propose an adaptive model with dual-dimensional attention for aero-engine RUL prognostics. Specifically, the dual-dimensional multi-head attention (DMHA) module is constructed to assign weights to features in the spatial and temporal dimensions, and then preforms the feature fusion. The mathematical derivations of the DMHA module parameter iterations are performed, which enhances the interpretability of the model. Next, the adaptive TCN (ATCN) is designed to further learn the time correlations of the fused features. Compared with the traditional TCN, the ATCN can adjust the network structure adaptively and extract the deep temporal representations better. Last, the prediction ability of the DMHA-ATCN is verified by conducting experiments on the C-MAPSS dataset.