Efficient recursive kernel principal component analysis for nonlinear time-varying processes monitoring

Shang, Liangliang; Yan, Ze; Qiu, Aibing; Li, Fēi; Zhou, Xinyi

doi:10.1109/ccdc.2019.8832617

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…Nonlinear principal component analysis (NLPCA) is a nonlinear generalization of standard PCA using the principal curve technique, which involves moving from straight lines to curves. To implement the NLPCA method, the authors of [12,13] developed an auto-associative neural network and simulated the dynamics of continuous chemical reactors using linear and nonlinear principal component methods. The nonlinear principal components are determined by a feedforward neural network with one hidden layer.…”

Section: Multivariate Statistical Process Control (Mspc)mentioning

confidence: 99%

Industrial Process Control Using DPCA and Hierarchical Pareto Optimization

Arsenyev,

Malykhina,

Shkodyrev

2023

Processes

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The control of large-scale industrial systems has several criteria, such as ensuring high productivity, low production costs and the lowest possible environmental impact. These criteria must be established for all subsystems of the large-scale system. This study is devoted to the development of a hierarchical control system that meets several of these criteria and allows for the separate optimization of each subsystem. Multicriteria optimization is based on the processing of data characterizing production processes, which makes it possible to organize a multidimensional statistical control process. Using neural networks to model the technological processes of subsystems and the method of dynamic principal component analysis (DPCA) to reduce the dimensionality of control problems allows us to find more efficient solutions. Using the example of a two-level hierarchy, we showed a variant of the connection between two subsystems by parameters.

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Section: Multivariate Statistical Process Control (Mspc)mentioning

confidence: 99%

Industrial Process Control Using DPCA and Hierarchical Pareto Optimization

Arsenyev,

Malykhina,

Shkodyrev

2023

Processes

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“…Other related works that utilize the moving window concept can be found in [190,[207][208][209]238,293]. A different adaptive approach is to use multivariate EWMA to update any part of the model, such as the kernel matrix, its eigen-decomposition, or the statistical indices [116,132,179,224,253,281,283,292]. Finally, for the dictionary learning approach by Fezai et al [246,247] (see Section 4.8), the Woodbury matrix identity is required to update the inverse of the kernel matrix, thereby updating the dictionary of kernel features as well.…”

Section: Time-varying Behavior and Adaptive Kernel Computationmentioning

confidence: 99%

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

et al. 2019

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Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries.

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