KPCA-Based Visual Fault Diagnosis for Nonlinear Industrial Process
文献类型：会议论文
| 作者 | Yu, Jiahui2; Gao HW(高宏伟)2
|
| 出版日期 | 2019 |
| 会议日期 | August 8-11, 2019 |
| 会议地点 | Shenyang, China |
| 关键词 | Fault diagnosis TE process KPCA Visualization system |
| 页码 | 145-154 |
| 英文摘要 | With 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. |
| 产权排序 | 2 |
| 会议录 | Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings
 |
| 会议录出版者 | Springer Verlag |
| 会议录出版地 | Berlin |
| 语种 | 英语 |
| ISSN号 | 0302-9743 |
| ISBN号 | 978-3-030-27540-2 |
| WOS记录号 | WOS:000569253700013 |
| 源URL | [http://ir.sia.cn/handle/173321/25506]  |
| 专题 | 沈阳自动化研究所_空间自动化技术研究室 |
| 作者单位 | 1.University of Portsmouth, Portsmouth PO1 3HE, United Kingdom 2.College of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China 3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China |
| 推荐引用方式 GB/T 7714 | Yu, Jiahui,Gao HW. KPCA-Based Visual Fault Diagnosis for Nonlinear Industrial Process[C]. 见:. Shenyang, China. August 8-11, 2019. |
入库方式： OAI收割
来源：沈阳自动化研究所
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