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Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction

机译：分层注意图卷积网络熔断多传感器信号，用于剩余使用寿命预测

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摘要

Deep learning-based prognostic methods have achieved great success in remaining useful life (RUL) prediction, since degradation information of machine can be adequately mined by deep learning techniques. However, these methods suffer from following weaknesses, that is, 1) interactions among multiple sensors are not explicitly considered; 2) they are more inclined to model temporal dependencies while ignoring spatial dependencies of sensors. To address those weaknesses, the multiple sensors are constructed to a sensor network and hierarchical attention graph convolutional network (HAGCN) is proposed in this paper for modeling the sensor network. In HAGCN, the hierarchical graph representation layer is proposed for modeling spatial dependencies of sensors and bi-directional long short-term memory network is used for modeling temporal dependencies of sensor measurements. Moreover, a regularized self-attention graph pooling is designed in HAGCN to achieve effective information fusion of the sensors. To realize prognostics, the spatial-temporal graphs are firstly generated based on the sensor network. Then, HAGCN is applied to model the spatial and temporal dependencies of the graphs simultaneously. The experimental results of two case studies show the superiority of HAGCN over state-of-the-art methods for RUL prediction.
机译：基于深度学习的预后方法在剩余的使用寿命（RUL）预测中取得了巨大成功，因为机器的退化信息可以通过深度学习技术充分开采。然而，这些方法遭受以下弱点，即多传感器之间的相互作用未明确考虑; 2）它们更倾向于模拟时间依赖性，同时忽略传感器的空间依赖性。为了解决这些缺点，多个传感器构造为传感器网络，并在本文中提出了分层关注图卷积网络（HAGCN），用于建模传感器网络。在HAGCN中，提出了用于建模传感器的空间依赖性和双向长短期存储器网络的分层图表示层用于建模传感器测量的时间依赖性。此外，在HAGCN中设计了一个正则化的自我关注图池，以实现传感器的有效信息融合。为了实现预后，首先基于传感器网络生成空间 - 时间图。然后，应用HAGCN以同时模拟图形的空间和时间依赖性。两种案例研究的实验结果表明了HAGCN在ruL预测的最先进方法中的优越性。

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