Abstract
When all process variables are used to establish one data-based model, some variables have little contributions to the model. When these variables are grouped into a small local variable set to develop a data-based model, their contributions will become large. It is called local variable characteristic. Standard canonical variate analysis (CVA) based process monitoring method doesn’t consider local process variable characteristic. To solve this problem, a variable sub-region based canonical variate analysis (V-CVA) is proposed for dynamic process monitoring by enhancing the information of local variables. The proposed method combines variable sub-region division and Bayesian fusion. First, standard CVA is performed by using all process variables. Then, K-means clustering is adopted to divide process variables into sub-regions based on the mutual information of process variables and canonical variables. For each variable sub-region, CVA is conducted to compute local process monitoring statistics. Finally, local statistics are fused to build ensemble statistics based on the idea of Bayesian inference. Compared with the standard CVA method, the proposed V-CVA method can emphasize the information of local variables, and has better monitoring performance in the canonical variable feature subspace and the prediction residual subspace. Two examples demonstrate the effectiveness of the proposed method.
Highlights
Process monitoring is of great importance to large scale continuous operating plants, such as oil refineries, chemical plants, and so on [1]
We present a variable sub-region canonical variate analysis (V-CVA) based dynamic process monitoring approach to enhance the information of local variables
A variable sub-region canonical variate analysis method has been proposed for dynamic process monitoring
Summary
Process monitoring is of great importance to large scale continuous operating plants, such as oil refineries, chemical plants, and so on [1]. Ku et al [17] used time shift data to augment the measured data matrix and proposed a dynamic PCA based process monitoring method. Faults related to those variables become difficult to be detected To solve this problem, we present a variable sub-region canonical variate analysis (V-CVA) based dynamic process monitoring approach to enhance the information of local variables. Our contribution is proposing a dynamic process monitoring method based on variable subregion canonical variate analysis. The proposed V-CVA is capable of considering process dynamic characteristic, making the information of local variables stand out, and achieving enhanced monitoring performance in the canonical variable feature subspace and the prediction residual subspace. Simulation results confirm the effectiveness of the proposed V-CVA based dynamic process monitoring approach The remainder of this contribution is organized as follows. S low dimensional canonical variable features can be obtained on the basis of (2)
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