Dynamic Causal Entropy-Spatiotemporal Convolutional Network for Quality-Related Fault Diagnosis of Large-Scale Industrial Processes
提出动态因果熵方法构建层次化动态因果图，结合3D挤压激励卷积网络分析时空特征，实现大规模工业过程的质量相关故障检测与根因识别，在热连轧过程数据上达到95.78%的检测准确率。
As large-scale industrial processes evolve toward greater complexity, the increasing interdependence of networked and dynamic process data has a critical impact on product quality, creating significant challenges for quality-related fault diagnosis. Causal graphs (CGs) are effective in modeling structural relationships among nodes in large-scale industrial processes. However, traditional causal discovery methods are limited in their ability to represent hierarchical and dynamic causal structures with spatiotemporal features. To overcome these limitations, a dynamic causal entropy (DCE)-spatiotemporal convolutional network is designed in this article. First, the proposed DCE method enables the construction of hierarchical dynamic CGs that accurately represent dynamic interactions among process variables, effectively mitigating confounding factors and enhancing interpretability. Second, a 3-D squeeze-and-excitation (SE) convolutional neural network is designed to adaptively recalibrate channel-wise information and deeply analyze the spatiotemporal characteristics embedded in the hierarchical dynamic CGs. Furthermore, a local-global quality-related fault detection approach is introduced, along with a novel causal anomaly vector that facilitates precise recognition of fault root causes across multiple hierarchical levels. Finally, the effectiveness and practical advantages of the proposed method are thoroughly demonstrated using both numerical simulations and real-world data from a hot strip mill process (HSMP), achieving a fault detection accuracy of 95.78%.