Abstract
Partial least squares (PLS) and linear regression methods are widely utilized for quality-related fault detection in industrial processes. Standard PLS decomposes the process variables into principal and residual parts. However, as the principal part still contains many components unrelated to quality, if these components were not removed it could cause many false alarms. Besides, although these components do not affect product quality, they have a great impact on process safety and information about other faults. Removing and discarding these components will lead to a reduction in the detection rate of faults, unrelated to quality. To overcome the drawbacks of Standard PLS, a novel method, MI-PLS (mutual information PLS), is proposed in this paper. The proposed MI-PLS algorithm utilizes mutual information to divide the process variables into selected and residual components, and then uses singular value decomposition (SVD) to further decompose the selected part into quality-related and quality-unrelated components, subsequently constructing quality-related monitoring statistics. To ensure that there is no information loss and that the proposed MI-PLS can be used in quality-related and quality-unrelated fault detection, a principal component analysis (PCA) model is performed on the residual component to obtain its score matrix, which is combined with the quality-unrelated part to obtain the total quality-unrelated monitoring statistics. Finally, the proposed method is applied on a numerical example and Tennessee Eastman process. The proposed MI-PLS has a lower computational load and more robust performance compared with T-PLS and PCR.
Highlights
Process quality monitoring methods can be divided into two types: Direct monitoring and processed monitoring
We propose a novel mutual information partial least squares (MI-Partial least squares (PLS))
false alarm rate (FAR) is the detection of faulty samples that are not related to quality, Y, whereas fault detection rate (FDR) indicates the detection of faulty samples relevant to quality Y
Summary
Process quality monitoring methods can be divided into two types: Direct monitoring and processed monitoring. Feature (variable) selection attempts to use only the most relevant quality variables of the process variable, eliminating weakly related and unrelated variables Such dimension reduction helps to reduce the computational complexity of the process, while improving the prediction accuracy and modeling efficiency. The partial least squares (PLS) method supervises quality variables, linearly decomposing the process variable to obtain a regression model that can extract the correlation between the process variable and the quality variable [15] It is often used for quality prediction and quality monitoring. Have been successfully applied in the process of the hot strip rolling industry, which has improved quality prediction accuracy and quality monitoring performance Both are based on linear conditions, and are only applicable to linear processes. We apply it to a numerical example and Tennessee Eastman (TE) process to verify its effectiveness
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