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.

中文翻译：

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质量相关和质量无关的故障检测的一种新的互信息和偏最小二乘方法

偏最小二乘（PLS）和线性回归方法已广泛用于工业过程中与质量相关的故障检测。标准PLS将过程变量分解为主要部分和剩余部分。但是，由于主要部分仍然包含许多与质量无关的组件，因此，如果不删除这些组件，可能会导致许多错误警报。此外，尽管这些组件不影响产品质量，但它们对过程安全性和其他故障信息有很大影响。删除和丢弃这些组件将导致故障检测率下降，而故障率与质量无关。为了克服标准PLS的缺点，本文提出了一种新的方法MI-PLS（互信息PLS）。提出的MI-PLS算法利用互信息将过程变量分为选定的分量和残差分量，然后使用奇异值分解（SVD）将选定的零件进一步分解为与质量相关和与质量无关的分量，随后构造与质量相关的分量监视统计信息。为了确保没有信息丢失，并且所提出的MI-PLS可以用于质量相关和质量无关的故障检测，对残差组件执行主成分分析（PCA）模型以获得其得分矩阵，与质量无关的部分相结合，以获得总的质量无关的监视统计信息。最后，将所提出的方法应用于数值实例和田纳西州伊士曼过程。