Abstract
Due to the closed-loop feedback control system, the data collected in the industry process is always temporal serial correlated. Moreover, different types of sensor data (such as temperature, pressure, liquid level, flow and so on) would show different temporal serial correlations. Meanwhile, the current process monitoring method considers all types of sensor data equally, ignoring the difference in temporal serial correlations caused by various sensor types. In response to this, serial correlated–uncorrelated concurrent space method is proposed in this work, where process variable original space is divided into serial correlated space and serial uncorrelated space via the degree of temporal serial correlations. In addition, the proposed method not only considers the local monitoring within space via principal component analysis and slow feature analysis respectively, but also monitors the information between-space based on the moving window and the mutual information. Finally, on the basis of the monitoring statistic within space and between-space, the comprehensive monitoring indicator is constructed via the local outlier factor method. The advantage and superiority of the proposed method is illustrated via testing on Tennessee Eastman process and Continuous Stirred Tank Reactor process.
Published Version
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