Accurate monitoring of mechanical device conditions requires a large number of sensors working together. There are potential connections between sensors throughout the degradation monitoring process of mechanical devices. Conventional deep learning (DL) models suffer from the following shortcomings when dealing with this type of multi-sensor degraded data. To begin with, most existing methods based on DL mainly use CNN as the feature extractor, focusing too much on temporal correlations and ignoring spatial correlations of multiple sensors. Then, the most popular remaining useful life (RUL) model is based on recurrent neural network, which oftentimes suffer from the issue of gradient exploding and vanishing. Therefore, a bran-new end-to-end framework based on a deep adaptative transformer enhanced by graph attention network, named GAT-DAT, is proposed to tackle these weaknesses. First, the graph data is constructed by the correlation of sensors. Next, GAT submodules fuse node features to extract spatial correlation. Finally, the DAT submodule is used to efficiently abstract the temporal features of the data through a self-attention mechanism and adaptively implements RUL prediction for mechanical equipment. Two case studies are employed to attest the efficacy of our proposed GAT-DAT model and the analysis of the experimental data illustrates that the GAT-DAT framework outperforms the existing state-of-the-art methods.

准确监测机械设备状态需要大量传感器协同工作。在整个机械设备的退化监测过程中，传感器之间存在潜在的连接。传统的深度学习 (DL) 模型在处理此类多传感器退化数据时存在以下缺点。首先，大多数现有的基于 DL 的方法主要使用 CNN 作为特征提取器，过分关注时间相关性而忽略了多个传感器的空间相关性。然后，最流行的剩余使用寿命 (RUL) 模型基于递归神经网络，它经常遇到梯度爆炸和消失的问题。因此，一个全新的基于图注意力网络增强的深度自适应转换器的端到端框架，命名为 GAT-DAT，建议解决这些弱点。首先，通过传感器的关联构建图形数据。接下来，GAT 子模块融合节点特征以提取空间相关性。最后，DAT子模块用于通过自注意力机制有效地抽象数据的时间特征，并自适应地实现机械设备的RUL预测。采用两个案例研究来证明我们提出的 GAT-DAT 模型的有效性，并且对实验数据的分析表明 GAT-DAT 框架优于现有的最先进方法。DAT子模块通过self-attention机制高效抽象数据的时间特征，自适应实现机械设备RUL预测。采用两个案例研究来证明我们提出的 GAT-DAT 模型的有效性，并且对实验数据的分析表明 GAT-DAT 框架优于现有的最先进方法。DAT子模块通过self-attention机制高效抽象数据的时间特征，自适应实现机械设备RUL预测。采用两个案例研究来证明我们提出的 GAT-DAT 模型的有效性，并且对实验数据的分析表明 GAT-DAT 框架优于现有的最先进方法。