Predicting Remaining Useful Life (RUL) plays a crucial role in the prognostics and health management of industrial systems that involve a variety of interrelated sensors. Given a constant stream of time series sensory data from such systems, deep learning models have risen to prominence at identifying complex, nonlinear temporal dependencies in these data. In addition to the temporal dependencies of individual sensors, spatial dependencies emerge as important correlations among these sensors, which can be naturally modelled by a temporal graph that describes time-varying spatial relationships. However, the majority of existing studies have relied on capturing discrete snapshots of this temporal graph, a coarse-grained approach that leads to loss of temporal information. Moreover, given the variety of heterogeneous sensors, it becomes vital that such inherent heterogeneity is leveraged for RUL prediction in temporal sensor graphs. To capture the nuances of the temporal and spatial relationships and heterogeneous characteristics in an interconnected graph of sensors, we introduce a novel model named Temporal and Heterogeneous Graph Neural Networks (THGNN). Specifically, THGNN aggregates historical data from neighboring nodes to accurately capture the temporal dynamics and spatial correlations within the stream of sensor data in a fine-grained manner. Moreover, the model leverages Feature-wise Linear Modulation (FiLM) to address the diversity of sensor types, significantly improving the model's capacity to learn the heterogeneity in the data sources. Finally, we have validated the effectiveness of our approach through comprehensive experiments. Our empirical findings demonstrate significant advancements on the N-CMAPSS dataset, achieving improvements of up to 19.2% and 31.6% in terms of two different evaluation metrics over state-of-the-art methods.

翻译：预测剩余寿命在涉及多种关联传感器的工业系统预测与健康管理中起着关键作用。针对此类系统产生的持续时间序列传感数据，深度学习模型在识别其中复杂的非线性时序依赖关系方面日益凸显其重要性。除单个传感器的时序依赖外，传感器间的空间依赖也表现为重要关联，这种关系可通过描述时变空间关联的时序图自然建模。然而，现有研究大多依赖对该时序图的离散快照捕获，这种粗粒度方法会导致时序信息损失。此外，考虑到异构传感器的多样性，在时序传感器图中利用这种固有异质性进行剩余寿命预测至关重要。为捕捉互联传感器图中时序-空间关系及异构特征的细微差异，我们提出名为时序异构图神经网络（THGNN）的新型模型。具体而言，THGNN通过聚合邻接节点的历史数据，以细粒度方式准确捕捉传感器数据流中的时序动态与空间关联。同时，模型采用特征级线性调制（FiLM）处理传感器类型多样性，显著提升了模型对数据源异构性的学习能力。最后，我们通过全面实验验证了方法的有效性。实验结果表明，在N-CMAPSS数据集上，本方法在两种不同评估指标上较现有最优方法分别实现了高达19.2%和31.6%的性能提升。