Learnable faster kernel-PCA for nonlinear fault detection: Deep autoencoder-based realization

doi:10.1016/j.jii.2024.100622

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Learnable faster kernel-PCA for nonlinear fault detection: Deep autoencoder-based realization
	Ren, Zelin1 ; Jiang, Yuchen 2; Yang, Xuebing1 ; Tang, Yongqiang1 ; Zhang, Wensheng1
发表期刊	JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
ISSN	2467-964X
	2024-07-01
卷号	40 页码:13
通讯作者	Ren, Zelin(rzl8816@126.com) ; Jiang, Yuchen(yc.jiang@hit.edu.cn)
摘要	Kernel principal component analysis (KPCA) is a well -recognized nonlinear dimensionality reduction method that has been widely used in nonlinear fault detection tasks. As a kernel trick -based method, KPCA inherits two major problems. First, the form and the parameters of the kernel function are usually selected blindly, depending seriously on trial -and -error. As a result, there may be serious performance degradation in case of inappropriate selections. Second, at the online monitoring stage, KPCA has much computational burden and poor real-time performance, because the kernel method requires to leverage all the offline training data. In this work, to deal with the two drawbacks, a learnable faster realization of the conventional KPCA is proposed. The core idea is to parameterize all feasible kernel functions using the novel nonlinear DAE-FE (deep autoencoder based feature extraction) framework and propose DAE-PCA (deep autoencoder based principal component analysis) approach in detail. The proposed DAE-PCA method is proved to be equivalent to KPCA but has more advantage in terms of automatic searching of the most suitable nonlinear high -dimensional space according to the inputs, which helps to improve the accuracy of fault detection. Furthermore, the online computational efficiency improves by many times compared with the conventional KPCA. Finally, the Tennessee Eastman (TE) process benchmark and wastewater treatment plant (WWTP) benchmark are employed to illustrate the effectiveness of the proposed method, where the average fault detection rates of DAE-PCA are at least 0.27% and 4.69% higher than those of other methods, and its online computational efficiency is faster 90.48% and 24.57% times than that of KPCA respectively.
关键词	Kernel Principal Component Analysis (KPCA) Fault detection Process monitoring Autoencoder Data-driven
DOI	10.1016/j.jii.2024.100622
关键词[WOS]	COMPONENT ANALYSIS
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62203143] ; Heilongjiang Provincial Projects[LJYXL2022-047] ; Heilongjiang Provincial Projects[LBH-Z22130]
项目资助者	National Natural Science Foundation of China ; Heilongjiang Provincial Projects
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS记录号	WOS:001239828000001
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/58658
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
通讯作者	Ren, Zelin; Jiang, Yuchen
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Harbin Inst Technol, Control & Simulat Ctr, Harbin 150001, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Ren, Zelin,Jiang, Yuchen,Yang, Xuebing,et al. Learnable faster kernel-PCA for nonlinear fault detection: Deep autoencoder-based realization[J]. JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION,2024,40:13.
APA	Ren, Zelin,Jiang, Yuchen,Yang, Xuebing,Tang, Yongqiang,&Zhang, Wensheng.(2024).Learnable faster kernel-PCA for nonlinear fault detection: Deep autoencoder-based realization.JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION,40,13.
MLA	Ren, Zelin,et al."Learnable faster kernel-PCA for nonlinear fault detection: Deep autoencoder-based realization".JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 40(2024):13.