A method for integrated monitoring of process multiple indicators based on quality‐aware network

Abstract The running status of production process often contains multiple evaluation indicators, including economy, output, energy consumption, and other evaluation indicators. Complex industrial process monitoring needs to consider the correlation and support of information between multiple indicators. This paper proposes a method for integrated monitoring of process multiple indicators based on quality‐aware network (QAN). This method introduces a correlation measure to screen the variables related to the indicators, and uses the self‐attention mechanism to mine the interconnection relationship between the variables, establishing the ‘quality‐source graph’ of multiple indicators. The method starts from the quality source graph space which is strongly correlated with the index, and uses the structural topology which contains the interconnection relationship between the process variables to realize the division of the index correlation feature space, which improves the fault detection ability of the method for single index correlation. Two quality‐source graph fusion methods are proposed to address varying emphasis on multi‐index monitoring in actual production: experience setting and machine self‐learning. Experiments on Tennessee Eastman process and ammonia synthesis process showed that QAN can fuse the quality source map through experience setting and self‐learning mechanism, and realize the comprehensive monitoring of process operation.