Integrated monitoring and diagnosis of industrial processes based on causality synergistic and unique decomposition

Abstract

Causality mining plays a crucial role in monitoring complex industrial processes. However, incomplete extraction of quality related information may lead to a reduced monitoring accuracy rate for quality related faults, while uncertain causal relationships during root variable mining can further result in wrong fault diagnosis outcomes. To address these problems, we decompose the causal relationships between variables into synergistic and unique ones and further propose an integrated monitoring and diagnosis approach for industrial processes based on causality synergistic and unique decomposition. Firstly, we use Granger causality to preliminarily identify quality-related features and enhance the extraction of quality related features via the synergistic effect of causal relationships for addressing their complex interdependence. Secondly, due to the synergistic causality among variables between variable groups, it is necessary to capture and model their dynamic characteristics to ensure monitoring accuracy. We extend quality variable fault monitoring to process variables and further achieve integrated monitoring. Finally, we explore causal uniqueness to identify the fault root cause, which is key to achieving precise and rapid diagnosis in complex and uncertain industrial processes. The feasibility and effectiveness of the proposed method were validated in two scenarios: the benchmark Tennessee Eastman (TE) chemical process and an industrial case study of poor iron ore beneficiation.