A hybrid specific index-related process monitoring strategy based on a novel two-step information extraction method

Abstract

A two-step information extraction method is presented to capture the specific index-related information more accurately. In the first step, the overall process variables are separated into two sets based on Pearson correlation coefficient. One is process variables strongly related to the specific index and the other is process variables weakly related to the specific index. Through performing principal component analysis (PCA) on the two sets, the directions of latent variables have changed. In other words, the correlation between latent variables in the set with strong correlation and the specific index may become weaker. Meanwhile, the correlation between latent variables in the set with weak correlation and the specific index may be enhanced. In the second step, the two sets are further divided into a subset strongly related to the specific index and a subset weakly related to the specific index from the perspective of latent variables using Pearson correlation coefficient, respectively. Two subsets strongly related to the specific index form a new subspace related to the specific index. Then, a hybrid monitoring strategy based on predicted specific index using partial least squares (PLS) and T² statistics-based method is proposed for specific index-related process monitoring using comprehensive information. Predicted specific index reflects real-time information for the specific index. T² statistics are used to monitor specific index-related information. Finally, the proposed method is applied to Tennessee Eastman (TE). The results indicate the effectiveness of the proposed method.

Keywords

specific index / hybrid monitoring strategy / two-step information extraction / subspace

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