Traceability of abnormal energy consumption modes in grinding systems based on evolution analysis of causal network structure

Abstract

Abnormal energy consumption mode tracing is used to locate the root cause of the system deviating from the normal energy consumption mode (ECM), in order to support operators in understanding system abnormal changes and abnormal propagation trends. Due to the compensatory effect of closed-loop control systems on external interference and the influence of processes such as coarse powder regrinding and circulating air utilization during grinding, there is a strong correlation between parameters, and anomalies are easily propagated between parameters. Tracing the root cause variable of anomalies is challenging. Previous abnormal ECM tracing ignored the dynamic changes in causal relationships between coupled variables in the circulation process, and had problems with strong expert knowledge dependence and slow response. Therefore, this paper proposes an abnormal ECM tracing method based on causal network structure evolution analysis. It proposes directed symbolic transfer entropy to measure variable causal relationships, and combines the advantages of complex networks in characterizing complex system dynamic characteristics to construct an ECM causal network. By analyzing network structure characteristics, it locates the root cause variables of abnormal ECMs and infers propagation paths. Finally, through the application of six types of abnormal scenarios in actual slag grinding processes, it verifies the accuracy of the proposed method in locating abnormal roots and identifying propagation paths.