Multi-objective monitoring of closed-loop controlled systems using adaptive LASSO

Wang, Yan; Yao, Yuan; Zheng, Ying; Wong, David Shan Hill

doi:10.1016/j.jtice.2015.05.010

Cited by 10 publications

(2 citation statements)

References 29 publications

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“…Tennessee-Eastman Benchmark Process. Tennessee-Eastman process (TE process) was put forward in 1993 by Downs and Vogel [35], which has been widely adopted for scientific research [36].…”

Section: 1mentioning

confidence: 99%

SPA-Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring

et al. 2021

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Many industrial processes are operated in multiple modes due to different manufacturing strategies. Multimodality of process data is often accompanied with nonlinear and non-Gaussian characteristics, which makes data-driven monitoring more complicated. In this paper, statistics pattern analysis (SPA) is introduced to extract low- and high-order statistics from raw process data. Support vector data description (SVDD), which can deal with nonlinear and non-Gaussian problems, is applied to monitor multimode process in this paper. To improve detection performance of SVDD for training multimode data with outliers, modified local reachability density ratio (mLRDR) is proposed as a weight factor to be embedded in the weighted-SVDD (wSVDD) model, in which the local neighbors in terms of both space and time are considered. Finally, the effectiveness and superiority of our proposed method are demonstrated by the Tennessee-Eastman (TE) process and wastewater treatment process (WWTP).

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“…Tennessee-Eastman Benchmark Process. Tennessee-Eastman process (TE process) was put forward in 1993 by Downs and Vogel [35], which has been widely adopted for scientific research [36].…”

Section: 1mentioning

confidence: 99%

SPA-Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring

et al. 2021

Self Cite

View full text Add to dashboard Cite

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“…[ 1–5 ] As a popular data‐driven method, multivariate statistical process monitoring has been widely applied in aerospace, chemical, semiconductor, and other industrial processes. [ 6–9 ] Among the multivariate statistical process monitoring methods, the method of principal component analysis (PCA) is the most basic one that has been widely studied. [ 10–12 ] By projecting process data into two different subspaces, PCA can effectively realize the dimension reduction and the extraction of latent feature.…”

Section: Introductionmentioning

confidence: 99%

A multi‐block NMF model for non‐Gaussian process monitoring based on the adaptive partition non‐negative matrix factorization and Bayesian inference

Wang

Ling

et al. 2020

Can J Chem Eng

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Non-negative matrix factorization (NMF) is a novel technique for dimensionreduction, which can be used to process data of non-Gaussian and Gaussian efficiently. A global NMF model is inappropriate for the whole process, since it neglects the local information and monitoring results are often hard to be interpreted. On the basis of adaptive partition non-negative matrix factorization (APNMF) and Bayesian inference, a multi-block NMF model for non-Gaussian process monitoring is put forward to detect and isolate the faults effectively. Using APNMF method, the original variables in different fault states can be adaptively divided into multiple sub-blocks, and on this basis, the NMF monitoring model of each sub-block is formed. Then, two new statistics are constructed by Bayesian inference to supply an intuitive display. Finally, a weighted reconstruction-based contribution (RBC) plot method is presented to reduce the smearing effect and find out the main causes of these faults. This method makes full use of the local and global information of process data and improves the effectiveness of process monitoring. The validity and feasibility of the proposed method will be proved by an example of a numerical process, a Tennessee Eastman (TE) benchmark process and a continuous stirred-tank reactor (CSTR) process.

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Detection of model-plant mismatch in closed-loop control system

Ling

Zheng

Zhang

et al. 2017

Journal of Process Control

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Multi-objective monitoring of closed-loop controlled systems using adaptive LASSO

Cited by 10 publications

References 29 publications

SPA-Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring

SPA-Based Modified Local Reachability Density Ratio wSVDD for Nonlinear Multimode Process Monitoring

A multi‐block NMF model for non‐Gaussian process monitoring based on the adaptive partition non‐negative matrix factorization and Bayesian inference

Detection of model-plant mismatch in closed-loop control system

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