Multi-phase batch process monitoring based on multiway weighted global neighborhood preserving embedding method

Hui, Yongyong; Zhao, Xiaoqiang

doi:10.1016/j.jprocont.2018.06.012

Cited by 22 publications

(14 citation statements)

References 36 publications

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“…Although the preprocessing procedure here is similar to the batch and variable hybrid unfolding method, their objectives are totally different 33–35 . In batch and variable hybrid unfolding, the final step is a variable‐wise rearrangement procedure, ie, the normalized matrix X ( I × KJ ) is rearranged to X ( KI × J ).…”

Section: Batch Process Monitoring Based On Mlaementioning

confidence: 99%

Batch process monitoring using multiway Laplacian autoencoders

Gao

Zheng

et al. 2020

Can J Chem Eng

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In this paper, a novel nonlinear method named multiway Laplacian autoencoder (MLAE) is proposed for batch process monitoring. Autoencoder (AE) is an effective unsupervised learning neural network for nonlinear feature extraction. Compared with traditional AEs, the proposed method has two main advantages. Firstly, traditional AEs usually ignore the local structure of the original dataset. The proposed MLAE method integrates graph Laplacian regularization to the loss function, and, thus, the local structure of the normal process data is fully considered. Secondly, the Laplacian matrix of the regularization term is constructed by an average local affinity matrix of all batch runs, which contains the information of the stochastic deviations among batches. Furthermore, two statistics, ie, H 2 and SPE statistics, are developed based on the extracted hidden representation and the retained reconstruction error. The effectiveness and advantages of the MLAE-based monitoring strategy are illustrated by a benchmark penicillin fermentation process and a real E. coli fermentation process.

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Section: Batch Process Monitoring Based On Mlaementioning

confidence: 99%

Batch process monitoring using multiway Laplacian autoencoders

Gao

Zheng

et al. 2020

Can J Chem Eng

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“…With the rapid changes of market demands, batch process with small batches, multiple varieties and high value-added products has received extensive attention (Chang et al, 2020;Hui and Zhao, 2018b;Zhang and Zhao, 2019). Typical batch processes may include food processing (Simoglou et al, 2005), injection molding (Jiang et al, 2019), biomedicine production (Zhu et al, 2019), and so forth.…”

Section: Introductionmentioning

confidence: 99%

Batch process monitoring based on global enhanced multiple neighborhoods preserving embedding

Yao

Zhao

et al. 2021

Transactions of the Institute of Measurement and Control

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Batch process generally has varying dynamic characteristic that causes low fault detection rate and high false alarm rate, and it is necessary and urgent to monitor batch process. This paper proposes a global enhanced multiple neighborhoods preserving embedding based fault detection strategy for dynamic batch process. Firstly, the angle neighbor is defined and selected to compensate for the insufficient expression for the spatial similarity of samples only by using the distance neighbor, and the time neighbor is introduced to describe the time correlations between samples. These three types of neighbors can fully characterize the similarity of the samples in time and space. Secondly, considering the minimum reconstruction error and the order information of three types of neighbors, an enhanced objective function is constructed to prevent the loss of order information when neighborhood preserving embedding (NPE) calculates the reconstruction weights. Furthermore, the enhanced objective function and a global objective function are organically combined to extract both global and local features, to describe process dynamics and visualize process data in a low-dimensional space. Finally, a monitoring index based on support vector data description is constructed to eliminate adverse effects of non-Gaussian data for monitoring performance. The advantages of the proposed method over principal component analysis, neighborhood preserving embedding, dynamic principal component analysis and time NPE are demonstrated by a numerical example and the penicillin fermentation process simulation.

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“…In order to build the process monitoring model via bilinear methods, the data must be converted into a two-way array before modelling. Typical bilinear methods include multiway principal component analysis (MPCA), [11] multiway partial least squares (MPLS), [12] multiway independent component analysis (MICA), [13] multiway weighted global neighbourhood preserving embedding, [14] the related nonlinear, dynamic, multiphase methods, [10,[15][16][17] and online process monitoring methods. [18] In a trilinear method model, the three-way data is dealt directly with tensor decomposition or functional regression, such as kernel tensor locality preserving projections, [19] parallel factor analysis (PARAFAC), [20] three-way decomposition, [21] GTucker2, and functional principal component analysis.…”

Section: Introductionmentioning

confidence: 99%

Quality relevant fault detection of batch process via statistical pattern and regression coefficient

Zhao

2021

Can J Chem Eng

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Compared with the normal process, the statistical values or the relationships between process variables and the quality that represent process situations would change in an abnormal batch. A novel method named quality relevant fault detection based on statistical pattern and regression coefficients (SPRC) is proposed for the batch process. Firstly, the statistical patterns of the process data, such as mean value and SD, are computed to quantify process characteristics. The regression model is built via linear methods, such as multiple linear regression, least absolute shrinkage and selection operator, to describe the linear relationship. The mutual information between the quality and the process parameters is used to characterize the nonlinear relationship. In this way, statistical patterns and regression coefficients which express batch information constitute the two-way matrix. Then, the matrix is dimensionally reduced by related method, such as principal component analysis. The relationships hidden in batch process could be sought by SPRC. Finally, two cases, penicillin fermentation and steel hot rolling, are used to validate the feasibility and advantages of the proposed method.

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Multi-phase batch process monitoring based on multiway weighted global neighborhood preserving embedding method

Cited by 22 publications

References 36 publications

Batch process monitoring using multiway Laplacian autoencoders

Batch process monitoring using multiway Laplacian autoencoders

Batch process monitoring based on global enhanced multiple neighborhoods preserving embedding

Quality relevant fault detection of batch process via statistical pattern and regression coefficient

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