Fault detection algorithm of batch process based on improved neighborhood preserving embedding-independent component analysis

doi:10.13196/j.cims.2021.04.010

	Fault detection algorithm of batch process based on improved neighborhood preserving embedding-independent component analysis
	Zhao, Xiaoqiang 1,2,3; Yao, Hongjuan 2
刊名	Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS
	2021-04-01
卷号	27 期号:4 页码:1062-1071
关键词	Batch data processing Bayesian networks Embeddings Gaussian distribution Gaussian noise (electronic) Independent component analysis Inference engines Signal detection Bayesian inference Detection effect Fault detection algorithm Local information Mixed distribution Neighborhood preserving embedding Non-Gaussian informations Penicillin production
ISSN号	10065911
DOI	10.13196/j.cims.2021.04.010
英文摘要	Aiming at the problem of bad fault detection effect of batch process because of its non-linearity and mixed distribution of Gaussian and non-Gaussian, Multi-way Differencial Neighborhood Preserving Embedding-Weighted and Differencial Independent Component Analysis (MDNPE-WDICA) algorithm for fault detection of batch process was proposed. The original data space was divided into Gaussian and non-Gaussian subspaces by Jarque-Bera testmethod (J-B test). In Gaussian subspace, MDNPE algorithm was proposed by combining differential strategy with NPE algorithm to preserve the local structure invariant and deal with the nonlinearity of data while the dimension of data was reduced, which could overcome the computational complexity caused by the introduction of the kernel function. In non-Gaussian subspace, WDICA algorithm was proposed by combining weighted differential strategy with ICA algorithm to solve the nonlinearity of data while the non-Gaussian information of data was fully extracted, and the local information of data was effectively used. A new monitoring statistic was established by Bayesian inference to realize fault detection for the whole batch process. The simulation results of penicillin production process demonstrated that the proposed algorithm was feasible and effective. © 2021, Editorial Department of CIMS. All right reserved.
语种	中文
出版者	CIMS
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/150889]
专题	电气工程与信息工程学院
作者单位	1.National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou; 730050, China 2.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China; 3.Gansu Provincial Key Laboratory of Advanced Control for Industrial Processes, Lanzhou; 730050, China;
推荐引用方式 GB/T 7714	Zhao, Xiaoqiang,Yao, Hongjuan. Fault detection algorithm of batch process based on improved neighborhood preserving embedding-independent component analysis[J]. Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS,2021,27(4):1062-1071.
APA	Zhao, Xiaoqiang,&Yao, Hongjuan.(2021).Fault detection algorithm of batch process based on improved neighborhood preserving embedding-independent component analysis.Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS,27(4),1062-1071.
MLA	Zhao, Xiaoqiang,et al."Fault detection algorithm of batch process based on improved neighborhood preserving embedding-independent component analysis".Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS 27.4(2021):1062-1071.