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Non-gaussian batch process monitoring based on MWSVDD of similarity measure | |
Zhao, Xiaoqiang1,2,3; W., Zhou | |
2019-03-20 | |
发表期刊 | Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) |
ISSN | 10010505 |
卷号 | 49期号:2页码:259-266 |
摘要 | Aiming at nonlinearity, multiphase and the Gaussian and non-Gaussian mixture distribution of process variables in batch processes, a multiway weighted support vector data description algorithm based on similarity measure MWSVDD(SmMWSVDD) was proposed in this paper. Firstly, the algorithm divided the multiphase process into a stable phase and a transitional phase by considering the similarity between phases. Then, a new kernel similarity weight was defined in high dimensional kernel space to balance all the radiuses obtained by support vector data description (SVDD) modeling, overcoming the shortcoming of the control limits constructed by SVDD. The mixture distribution was divided into Gaussian distribution and non-Gaussian distribution variables by a D-test method to be modeled and monitored using multiway kernel principal component analysis (MKPCA) and improved SVDD. Finally, the integration unified monitoring statistic was built at each phase by Bayesian inference and verified by the penicillin fermentation process. The result shows that the proposed algorithm can reduce the false alarm rate by 20.21% and the missed alarm rate by 10.27% on average than MKPCA and SVDD. Thus, it is more effective for multiphase and mixture distributional batch process monitoring. © 2019, Editorial Department of Journal of Southeast University. All right reserved. |
关键词 | Bayesian networks Data description Gaussian distribution Gaussian noise (electronic) Inference engines Principal component analysis Process control Process monitoring Testing Vector spaces Batch process Mixture distributions Multiphase Similarity measure Support vector data description |
DOI | 10.3969/j.issn.1001-0505.2019.02.009 |
收录类别 | EI |
语种 | 中文 |
出版者 | Southeast University |
EI入藏号 | 20192106970974 |
EI主题词 | Batch data processing |
EI分类号 | 723.2 Data Processing and Image Processing - 723.4.1 Expert Systems - 913.1 Production Engineering - 921 Mathematics - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory - 922.1 Probability Theory |
来源库 | Compendex |
分类代码 | 723.2 Data Processing and Image Processing - 723.4.1 Expert Systems - 913.1 Production Engineering - 921 Mathematics - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory - 922.2 Mathematical Statistics |
引用统计 | 无
|
文献类型 | 期刊论文 |
条目标识符 | https://ir.lut.edu.cn/handle/2XXMBERH/113944 |
专题 | 电气工程与信息工程学院 |
作者单位 | 1.College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China; 2.Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou; 730050, China; 3.National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou; 730050, China |
第一作者单位 | 兰州理工大学 |
第一作者的第一单位 | 兰州理工大学 |
推荐引用方式 GB/T 7714 | Zhao, Xiaoqiang,W., Zhou. Non-gaussian batch process monitoring based on MWSVDD of similarity measure[J]. Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition),2019,49(2):259-266. |
APA | Zhao, Xiaoqiang,&W., Zhou.(2019).Non-gaussian batch process monitoring based on MWSVDD of similarity measure.Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition),49(2),259-266. |
MLA | Zhao, Xiaoqiang,et al."Non-gaussian batch process monitoring based on MWSVDD of similarity measure".Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) 49.2(2019):259-266. |
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