Hierarchical Spatiotemporal Characteristics Analysis-Based Quality-Related Fault Detection and Trustworthy Diagnosis for Complex Industrial Processes

With the improvement of production efficiency and the demand for product diversification, industrial processes present the characteristics of large-scale, long processes, and multiprocess coupling. The analysis of hierarchical spatiotemporal characteristics, the imbalanced sample condition, and the uncertainty caused by out-of-distribution (OOD) scenarios including the changes of operating modes or new fault conditions bring severe challenges to traditional fault detection and diagnosis methods. In this article, an integrated quality-related fault detection and trustworthy diagnosis scheme based on hierarchical spatiotemporal characteristic analysis is proposed. First, spatiotemporal graphs are formulated with reference to the spatial topology of industrial processes and the temporal correlation of data. A cascade spatiotemporal graph neural network (CSTGN) is proposed to extract intra-subprocess and inter-subprocess hierarchical spatiotemporal characteristics, and a bottleneck layer is designed to extract quality-related latent variables from the hierarchical spatiotemporal characteristics to achieve fault detection. Second, considering the imbalanced sample condition in real industrial processes, the CSTGN is transferred to the few-shot fault diagnosis task through model-based transfer learning. Finally, OOD scenarios may emerge subsequent to the deployment of the trained mode. Therefore, Bayesian deep learning is introduced into the CSTGN to perceive the uncertainty and achieve trustworthy fault diagnosis. The effectiveness of the proposed method is illustrated with a real hot strip mill process (HSMP).