Optimizing a vector of shrinkage factors for continuum regression

Xie, Zhonghao; Feng, Xiaoyi; Chen, Xiaojing; Huang, Guangzao

doi:10.1016/j.chemolab.2020.104141

Cited by 2 publications

(3 citation statements)

References 31 publications

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“…Thus, PLS achieves better data analysis than CCA under the same conditions. 14 PLS takes into account not only the correlation between the process and quality variables (correlation coefficient) but also the fact that the extracted latent variables can represent the characteristics of the process-variable and quality-variable spaces (variance) as much as possible. From this perspective, it is more general to generalize PLS to non-Gaussian data analysis.…”

Section: Introductionmentioning

confidence: 99%

“…It is known that PLS integrates the objectives of PCR and CCA and seems to treat both equally, and PCR determines the direction that best explains the process variables. Thus, PLS achieves better data analysis than CCA under the same conditions . PLS takes into account not only the correlation between the process and quality variables (correlation coefficient) but also the fact that the extracted latent variables can represent the characteristics of the process-variable and quality-variable spaces (variance) as much as possible.…”

Section: Introductionmentioning

confidence: 99%

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Non-Gaussian Quality Relevant Process Monitoring Based on Higher-Order Statistics Projection to the Latent Structure and Independent Signal Correction

Zhou

Yang

2023

Ind. Eng. Chem. Res.

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A novel statistical model based on the higher-order statistics projection to the latent structure (HPLS) is proposed, which uses a combination of higher-order statistics (mutual information and differential entropy) instead of covariance to extract latent variables. This model not only has an explicit representation similar to the projection to latent structure model but can also capture the non-Gaussian process features. Furthermore, in order to reduce the redundant components contained in the latent variables independent of the quality variables, a novel strategy called independent signal correction is proposed. Finally, the novel independent signal correction HPLS (ISC-HPLS) model and the corresponding process monitoring strategy are developed. This model considers higher-order statistics that may involve certain key features of non-Gaussian processes and eliminates redundant components. Experimental results from synthetic numerical simulations and the Tennessee-Eastman process benchmark test verified the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non-Gaussian Quality Relevant Process Monitoring Based on Higher-Order Statistics Projection to the Latent Structure and Independent Signal Correction

Zhou

Yang

2023

Ind. Eng. Chem. Res.

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“…CCA analyzes the relationship between two sets of variables after projection to reflect the degree of correlation, but this does not directly reveal the mapping of the two sets of variables . PLS takes into account the ideas of PCA and CCA and tries to find the multidimensional direction in the X-space to explain the multidimensional direction with the largest variance in the Y-space, which has gained wide application. , However, PLS mainly studies the variance information among data, resulting in local structural information being ignored. Due to the impact of the structural relationship among the data on dimensionality reduction, the loss of this information may affect the results after projection mapping, thereby affecting the monitoring results.…”

Section: Introductionmentioning

confidence: 99%

Quality-Relevant Process Monitoring with Concurrent Locality-Preserving Dynamic Latent Variable Method

Zhang

Xie

et al. 2022

ACS Omega

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A concurrent locality-preserving dynamic latent variable (CLDLV) method is proposed to extract the correlation between process variables and quality variables for quality-related dynamic process monitoring. Given that dynamic process data can easily be contaminated by noise and outliers and conventional dynamic latent variable models lack robustness, a low-rank autoregressive model is developed to deal with autocorrelation and cross-correlation properties among the data. Then neighborhood structure information is integrated into the partial least squares model, which can better reveal the essential structure of the data. The final concurrent projection of the latent structures is employed to monitor output-related faults and input-related process faults that affect quality. The Tennessee Eastman process and hot strip mill process are used to demonstrate the effectiveness of CLDLV-based detection and diagnostic methods.

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Optimizing a vector of shrinkage factors for continuum regression

Cited by 2 publications

References 31 publications

Non-Gaussian Quality Relevant Process Monitoring Based on Higher-Order Statistics Projection to the Latent Structure and Independent Signal Correction

Non-Gaussian Quality Relevant Process Monitoring Based on Higher-Order Statistics Projection to the Latent Structure and Independent Signal Correction

Quality-Relevant Process Monitoring with Concurrent Locality-Preserving Dynamic Latent Variable Method

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