Spatial-Temporal Causality Modeling for Industrial Processes With a Knowledge-Data Guided Reinforcement Learning

• Published in: IEEE transactions on industrial informatics Vol. 20; no. 4; pp. 5634 - 5646
• Main Authors: Zhang, Xiangrui, Song, Chunyue, Zhao, Jun, Xu, Zuhua, Deng, Xiaogang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-04-2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anomalies; Anomaly detection; Argon; causal discovery; Causality; Data models; Delay effects; Distillation; Graph theory; Logic gates; Markov processes; Modelling; Optimization; Process control; Process modeling; Process variables; reinforcement learning (RL); Reliability; soft sensor; spatial-temporal causality
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2023.3333921

Causality in an industrial process provides insights into how various process variables interact and affect each other within the system. It reveals the underlying mechanisms of industrial processes, which ensures predictive reliability and facilitates physical interpretability. However, existing causality-based techniques have limitations, as they neglect the temporal factor in causal description, introduce spurious causal associations in causal discovery, and fail to consider the spatial-temporal synchronicity in causal utilization. To address these issues, this article proposes a spatial-temporal causality modeling approach. A novel spatial-temporal causal digraph (STCG) is proposed to describe causal dependencies among process variables, which considers both spatial and temporal factors encompassing causal relationships and time delays. The STCG identification procedure is formulated as a Markov decision process, and knowledge-data guided reinforcement learning is developed to acquire the optimal identification policy and avoid spurious causal associations. With the identified STCG, a graph attention gate recurrent unit (GAGRU) is constructed for spatial-temporal process modeling, which is able to capture the synchronous evolution of industrial data in spatial-temporal dimensions. Finally, the effectiveness of the proposed modeling approach is verified by applying to two real industrial cases, including soft sensing for a sulfur recovery unit and anomaly detection for an argon distillation system. The experimental results demonstrate that the STCG-based industrial process modeling outperforms classical and state-of-the-art comparison methods in terms of reliability and interpretability.