Randomized Kernel Principal Component Analysis for Modeling and Monitoring of Nonlinear Industrial Processes with Massive Data

Zhou, Zhe; Du, Ni; Xu, Jingyun; Li, Zuxin; Jie, Zhang

doi:10.1021/acs.iecr.9b00300

Cited by 16 publications

(9 citation statements)

References 39 publications

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“…Dimension reduction was vital to the accuracy of the model because of the strong coupling and redundancy in the indicator system. PCA was able to merge the original features and reduce the dimension to simplify computation, especially aiming at strong linear indicators [50,51]. When the data were processed by PCA, only some principal influencing factors were considered.…”

Section: The Principle Of the Pca-ga-bp Neural Networkmentioning

confidence: 99%

Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions

Deng

Xie

et al. 2022

Sustainability

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Evaluating the regional trends of air pollution disaster risk in areas of heavy industry and economically developed cities is vital for regional sustainable development. Until now, previous studies have mainly adopted a traditional weighted comprehensive evaluation method to analyze the air pollution disaster risk. This research has integrated principal component analysis (PCA), a genetic algorithm (GA) and a backpropagation (BP) neural network to evaluate the regional disaster risk. Hazard risk, hazard-laden environment sensitivity, hazard-bearing body vulnerability and disaster resilience were used to measure the degree of disaster risk. The main findings were: (1) the air pollution disaster risk index of Liaoning Province, Beijing, Shanghai and Guangdong Province increased year by year from 2010 to 2019; (2) the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) of each regional air pollution disaster risk index in 2019, as predicted by the PCA-GA-BP neural network, were 0.607, 0.317 and 20.3%, respectively; (3) the predicted results were more accurate than those using a PCA-BP neural network, GA-BP neural network, traditional BP neural network, support vector regression (SVR) or extreme gradient boosting (XGBoost), which verified that machine learning could be used as a method of air pollution disaster risk assessment to a considerable extent.

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Section: The Principle Of the Pca-ga-bp Neural Networkmentioning

confidence: 99%

Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions

Deng

Xie

et al. 2022

Sustainability

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“…For more information, see the theoretical and empirical comparison of the Nyström method and random Fourier features by Yang et al [318]. Other related low-rank approximation schemes were proposed by Peng et al [283] which applies to kernel ICA, and that of Zhou et al [286] called randomized kernel PCA. Lastly, a different approximation using the Taylor expansion of the RBF kernel was also derived by Wang et al [288,289], and was called kernel sample equivalent replacement.…”

Section: Fast Computation Of Kernel Featuresmentioning

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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“…Lahdhiri et al (2018) developed a novel reduced Rank-KPCA to eliminate the dependencies of variables in the feature space and retain a reduce data from the original one, but it needs a large amount of training data to construct the reduced Rank-KPCA model. Zhou et al (2019) considered the problem of poor performance of a KPCA model constructed from the reduced sample set in monitoring nonlinear industrial process and proposed randomized KPCA for monitoring nonlinear industrial processes with massive data.…”

Section: Introductionmentioning

confidence: 99%

The instrument fault detection and identification based on kernel principal component analysis and coupling analysis in process industry

Liang

Gao

et al. 2020

Transactions of the Institute of Measurement and Control

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This paper investigates the fault detection problem of instruments in process industry. Considering the difficulty of fault identification and the problems of multivariable and large computation complexity based on traditional kernel principal component analysis (KPCA), this paper presents a new method for fault detection and identification, which combines the coupling analysis with kernel principal component for multivariable fault detection and employed the local outlier factor (LOF) for multivariable fault identification. The new method consists of three parts. Firstly, according to nonlinear correlation of multivariable, coupling analysis and module division of variables based on detrended cross-correlation analysis (DCCA) are considered to reduce false alarm rate (FAR) and missed detection rate (MDR) in fault detection and identification. Secondly, KPCA is employed to detect fault in each sub-module of variables. Finally, for the sub-module which has the fault detected in second step, the LOF is adopted to calculate abnormal contribution of each variable in sub-modules to realize fault identification. To prove that the new method has the better capability of processing multivariable fault detection and the more accuracy rate on fault detection and identification than the conventional methods of KPCA, a case study on Tennessee process is carried out at the end.

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Randomized Kernel Principal Component Analysis for Modeling and Monitoring of Nonlinear Industrial Processes with Massive Data

Cited by 16 publications

References 39 publications

Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions

Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions

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

The instrument fault detection and identification based on kernel principal component analysis and coupling analysis in process industry

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