Knowledge-data-driven process monitoring based on temporal knowledge graphs and supervised contrastive learning for complex industrial processes

Abstract

Process monitoring detects faults and issues alerts when faults occur. It has become an integral part of ensuring the safety and quality of industrial processes. Existing mainstream process-monitoring methods often separate data from knowledge, forming distinct systems. However, data and knowledge exhibit complementary characteristics, and using them together can contribute to enhancing monitoring performance. Furthermore, the importance of fault data has not been adequately emphasized. Within this fault data, valuable fault features contribute significantly to process monitoring. In light of these considerations, we propose a process-monitoring method based on temporal knowledge graphs and supervised contrastive learning,which can fully use knowledge, data, and fault information to improve the monitoring performance of the model. First, a temporal knowledge graph is constructed, in which knowledge and data are organically integrated through qualitative knowledge and quantitative data calculations to enhance the interpretability and accuracy of the graph. Second, spatiotemporal features are extracted from the temporal knowledge graph at multiple levels through differentiable graph pooling. Finally, a monitoring statistic is constructed, and fault information is introduced into the statistic through supervised contrastive learning, using fault information to enhance monitoring performance of the model. The fault detection rate on the float-glass production process reaches 95%.