SIA OpenIR  > 空间自动化技术研究室
KPCA-Based Visual Fault Diagnosis for Nonlinear Industrial Process
Yu, Jiahui1; Gao HW(高宏伟)1; Ju ZJ(琚兆杰)2,3
Department空间自动化技术研究室
Conference Name12th International Conference on Intelligent Robotics and Applications, ICIRA 2019
Conference DateAugust 8-11, 2019
Conference PlaceShenyang, China
Source PublicationIntelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings
PublisherSpringer Verlag
Publication PlaceBerlin
2019
Pages145-154
Indexed ByEI
EI Accession number20193407335848
Contribution Rank2
ISSN0302-9743
ISBN978-3-030-27540-2
KeywordFault diagnosis TE process KPCA Visualization system
AbstractWith the increasingly large-scale, continuous, and complicated chemical process, it is particularly important to ensure the stability and safety of the production process. However, in past studies, the accuracy of fault diagnosis and the degree of system visualization are still insufficient. Here, in order to solve these problems, a visual fault diagnosis system based on LabVIEW and Matlab is designed. First, the system uses LabVIEW interface design, applying Matlab to compile the algorithm program, which makes the system has a powerful data calculation and processing functions, as well as a clear visual interface, the system design also optimizes the communication interface. Second, the typical chemical production process TE (Tennessee Eastman) process is the subject of systematic testing. Additionally, because most of the industrial processes are non-linear, the fault diagnosis method based on Kernel Principal Component Analysis (KPCA) is used in the system design, and the implementation process of this method is elaborated. Finally, the system achieves the functions of TE process data acquisition, data preprocessing, and fault diagnosis lamps. A large number of simulation results verify the effectiveness of the proposed method. The system has entered the stage of laboratory application and provides a good application platform for the research of fault diagnosis of complex systems such as chemical process control.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/25506
Collection空间自动化技术研究室
Corresponding AuthorJu ZJ(琚兆杰)
Affiliation1.College of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Portsmouth, Portsmouth PO1 3HE, United Kingdom
Recommended Citation
GB/T 7714
Yu, Jiahui,Gao HW,Ju ZJ. KPCA-Based Visual Fault Diagnosis for Nonlinear Industrial Process[C]. Berlin:Springer Verlag,2019:145-154.
Files in This Item:
File Name/Size DocType Version Access License
KPCA-Based Visual Fa(1328KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yu, Jiahui]'s Articles
[Gao HW(高宏伟)]'s Articles
[Ju ZJ(琚兆杰)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yu, Jiahui]'s Articles
[Gao HW(高宏伟)]'s Articles
[Ju ZJ(琚兆杰)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yu, Jiahui]'s Articles
[Gao HW(高宏伟)]'s Articles
[Ju ZJ(琚兆杰)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: KPCA-Based Visual Fault Diagnosis for Nonlinear Industrial Process.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.