多项傅里叶图神经网络与样本关系学习用于增强剩余使用寿命预测

中文摘要 AI 辅助

预测剩余使用寿命（RUL）对于有效的预测性维护至关重要。时空图神经网络（ST-GNNs）通过将时间序列数据表示为一系列图来建模时间和空间关系，在RUL预测中表现出卓越的性能。然而，当前的ST-GNNs存在几个缺点。首先，它们需要领域专业知识或大量的计算能力来在部署GNN之前建立图结构。其次，模型仅限于在预定义的固定大小回看窗口内捕获时间依赖性。这种限制忽略了时间序列长度变化的常见问题，导致预测模型遗漏短期或长期依赖性。最后，传统模型往往无法捕获从相邻时间窗口生成的样本之间的固有关系，而这些关系对于提高预测的准确性和鲁棒性至关重要。为了解决上述问题，我们引入了一种名为多项傅里叶图神经网络与样本关系学习（MTFGN-SRL）的新框架。我们不将样本视为一系列图，而是将其视为一个完整的图，并利用傅里叶图神经网络（FGN）在频域中捕获时空信息。我们提出了一个多项学习模块，利用多个回看窗口生成具有不同项的样本，然后将这些样本输入FGN以增强从数据中提取有用信息。最后，我们通过训练一个异构图神经网络来识别样本间关系，开发了一个样本关系学习模块，从而提高了预测的准确性和鲁棒性。在CMAPSS数据集上的评估表明，MTFGN-SRL在RUL预测中优于最先进的方法。

英文摘要

Predicting the remaining useful life (RUL) is essential for effective predictive maintenance. Spatio-Temporal Graph Neural Networks (ST-GNNs), which can model both temporal and spatial relationships by representing time series data as a sequence of graphs, have shown exceptional performance in RUL prediction. However, current ST-GNNs face several drawbacks. First, they require domain expertise or significant computational power to establish graph structures prior to deploying GNNs. Second, the models are restricted to capture temporal dependencies within a predefined fixed-size lookback window. This restriction ignores the common issue of varying time series lengths, leading the prediction model to miss short-term or long-term dependencies. Finally, conventional models often fail to capture the inherent relationships between samples generated from adjacent time windows, which are crucial for improving both the accuracy and robustness of predictions. To address the aforementioned issues, we introduce a novel framework called Multi-Term Fourier Graph Neural Network with Sample Relationship Learning (MTFGN-SRL). Rather than treating the sample as a sequence of graphs, we consider it as a single complete graph and utilize a Fourier Graph Neural Network (FGN) to capture the spatio-temporal information in the frequency domain. We propose a multi-term learning module that utilizes multiple lookback windows to generate samples with varying terms, which are then fed into the FGN to enhance the extraction of useful information from the data. Finally, we develop a sample relationship learning module by training a heterogeneous GNN to identify inter-sample relationships, resulting in enhanced accuracy and robustness in predictions. Evaluations on the CMAPSS dataset demonstrate MTFGN-SRL's superior performance over state-of-the-art methods in RUL prediction.

发表机构

• Eindhoven University of Technology（埃因霍温理工大学）

机构由 AI 辅助整理，请以论文原文为准。