Abstract
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.
Abstract (translated)
预测剩余使用寿命（RUL）在涉及多种相关传感器的工业系统的预诊和健康管理中扮演着关键角色。给定不断从这些系统中涌现的时间序列传感器数据流，深度学习模型已经崛起，用于识别这些数据中的复杂非线性时间依赖关系。除了单个传感器的時間依賴關係之外，還出现了这些传感器之间的空间依賴關係，這些依賴關係可以自然地用描述时间变化的图来建模。然而，现有的研究主要依赖于捕捉这个时间图的离散快照，这是一种粗粒度的方法，导致丢失了时间信息。此外，考虑到传感器的不均匀性，在传感器图中利用固有异质性对于RUL预测至关重要。为了捕捉传感器之间的时间和非线性关系以及异质特征，我们引入了一种名为 Temporal and Heterogeneous Graph Neural Networks (THGNN) 的新模型。具体来说，THGNN通过聚合邻近节点的 historical 数据，准确地捕捉传感器数据流中的时间动态和空间关联。此外，模型利用特征级线性变换（FiLM）来解决传感器类型的异质性，显著提高了模型学习数据源异质性的能力。最后，我们通过全面的实验验证了我们的方法的有效性。我们的实证研究结果表明，在 N-CMAPSS 数据集上取得了显著的进展，将先进的预估方法的性能提高了 19.2% 和 31.6%。
URL
https://arxiv.org/abs/2405.04336
