Remaining useful life prediction plays a crucial role in the health management of industrial systems. Given the increasing complexity of systems, data-driven predictive models have attracted significant research interest. Upon reviewing the existing literature, it appears that many studies either do not fully integrate both spatial and temporal features or employ only a single attention mechanism. Furthermore, there seems to be inconsistency in the choice of data normalization methods, particularly concerning operating conditions, which might influence predictive performance. To bridge these observations, this study presents the Spatio-Temporal Attention Graph Neural Network. Our model combines graph neural networks and temporal convolutional neural networks for spatial and temporal feature extraction, respectively. The cascade of these extractors, combined with multi-head attention mechanisms for both spatio-temporal dimensions, aims to improve predictive precision and refine model explainability. Comprehensive experiments were conducted on the C-MAPSS dataset to evaluate the impact of unified versus clustering normalization. The findings suggest that our model performs state-of-the-art results using only the unified normalization. Additionally, when dealing with datasets with multiple operating conditions, cluster normalization enhances the performance of our proposed model by up to 27%.

翻译：剩余寿命预测在工业系统健康管理中扮演着关键角色。随着系统复杂性的日益增长，数据驱动的预测模型引发了显著的研究兴趣。在回顾现有文献后发现，许多研究要么未能充分整合空间和时间特征，要么仅采用单一的注意力机制。此外，在数据归一化方法的选择上存在不一致性，尤其是涉及运行工况时，这可能会影响预测性能。为弥补这些不足，本研究提出了时空注意力图神经网络。该模型结合图神经网络和时间卷积神经网络分别进行空间和时间特征提取，通过级联这些特征提取器并结合用于时空维度的多头注意力机制，旨在提高预测精度并增强模型可解释性。在C-MAPSS数据集上开展了全面实验，以评估统一归一化与聚类归一化的影响。结果表明，仅采用统一归一化时，我们的模型即可达到最先进水平。此外，在处理多运行工况数据集时，聚类归一化可使所提模型的性能提升高达27%。