用因果图结构提升工业过程监控的可靠性与可解释性。
Graph Autoencoder for Process Monitoring
- 通过自注意力机制学习变量动态关联，构建相关图。
- 提出三步算法从相关图中挖掘不变因果图，提升诊断可信度。
- 结合时空编码器解码器实现故障检测，适合工业异常预警场景。
为提升工业过程监控的可靠性和可解释性，本文提出因果图时空自编码器（CGSTAE）。该模型由基于空间自注意力机制（SSAM）的相关图结构学习模块和基于图卷积长短期记忆（GCLSTM）的时空编码器-解码器模块组成。SSAM通过捕捉变量间的动态关系学习相关图，随后引入一种新颖的三步因果图结构学习算法，利用因果不变性原理的逆视角，从变化的相关图中提取出稳定的因果图。时空编码器-解码器在序列到序列框架下重构时序过程数据。通过特征空间与残差空间中的两个统计量，CGSTAE实现有效的过程监控与故障检测。实验在Tennessee Eastman过程和真实空气分离过程上验证了其有效性。
原文摘要 · Abstract (English)
To improve the reliability and interpretability of industrial process monitoring, this article proposes a Causal Graph Spatial-Temporal Autoencoder (CGSTAE). The network architecture of CGSTAE combines two components: a correlation graph structure learning module based on spatial self-attention mechanism (SSAM) and a spatial-temporal encoder-decoder module utilizing graph convolutional long-short term memory (GCLSTM). The SSAM learns correlation graphs by capturing dynamic relationships between variables, while a novel three-step causal graph structure learning algorithm is introduced to derive a causal graph from these correlation graphs. The algorithm leverages a reverse perspective of causal invariance principle to uncover the invariant causal graph from varying correlations. The spatial-temporal encoder-decoder, built with GCLSTM units, reconstructs time-series process data within a sequence-to-sequence framework. The proposed CGSTAE enables effective process monitoring and fault detection through two statistics in the feature space and residual space. Finally, we validate the effectiveness of CGSTAE in process monitoring through the Tennessee Eastman process and a real-world air separation process.
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