Learnable faster kernel-PCA for nonlinear fault detection: Deep autoencoder-based realization | |
Ren, Zelin2; Jiang, Yuchen1; Yang, Xuebing2; Tang, Yongqiang2; Zhang, Wensheng2 | |
刊名 | JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
![]() |
2024-07-01 | |
卷号 | 40页码:13 |
关键词 | Kernel Principal Component Analysis (KPCA) Fault detection Process monitoring Autoencoder Data-driven |
ISSN号 | 2467-964X |
DOI | 10.1016/j.jii.2024.100622 |
通讯作者 | Ren, Zelin(rzl8816@126.com) ; Jiang, Yuchen(yc.jiang@hit.edu.cn) |
英文摘要 | 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. |
资助项目 | National Natural Science Foundation of China[62203143] ; Heilongjiang Provincial Projects[LJYXL2022-047] ; Heilongjiang Provincial Projects[LBH-Z22130] |
WOS关键词 | COMPONENT ANALYSIS |
WOS研究方向 | Computer Science ; Engineering |
语种 | 英语 |
出版者 | ELSEVIER |
WOS记录号 | WOS:001239828000001 |
资助机构 | National Natural Science Foundation of China ; Heilongjiang Provincial Projects |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/58657] ![]() |
专题 | 精密感知与控制研究中心_人工智能与机器学习 |
通讯作者 | Ren, Zelin; Jiang, Yuchen |
作者单位 | 1.Harbin Inst Technol, Control & Simulat Ctr, Harbin 150001, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
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. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论