Abstract
Aiming at the problem that the value density of variables in the state-aware network of complex industry system is low, which leads to poor timeliness of state evaluation, a key variable screening method for state-aware network is proposed. Firstly, a causal network model that can accurately reflect the interaction mode between the monitoring variables of the system is established. Secondly, each node of the causal network can be ranked by the LeaderRank algorithm and the variable set can be divided into multiple sets of variables. Finally, each variable set can be used to evaluate the performance state of the system, and the effectiveness index of variable screening is constructed to evaluate the accuracy and timeliness of the evaluation results of each variable set, then the key variables can be obtained. The Tennessee Eastman (TE) process data is used to test the proposed method, the result shows that the key variables obtained by the method can effectively improve the evaluation efficiency of the performance state.
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