Markov Chain Neighborhood Sparse Preserving Graph Embedding Based on Tensor Factorization for Batch Process Monitoring

doi:10.1109/ACCESS.2021.3052197

	Markov Chain Neighborhood Sparse Preserving Graph Embedding Based on Tensor Factorization for Batch Process Monitoring
	Zhao, Xiaoqiang; Mou, Miao
刊名	IEEE Access
	2021
卷号	9 页码:16211-16224
关键词	Batch data processing Dimensionality reduction Embeddings Factorization Graph algorithms Graph theory Process control Process monitoring Tensors Batch process monitoring Graph embeddings Information loss Markov chain analysis Monitoring results Simulation process Tensor factorization Three dimensions
ISSN号	2169-3536
DOI	10.1109/ACCESS.2021.3052197
英文摘要	If the three-dimension data of batch process are unfolded the two-dimension data, some important information would lose, and outliers such as noise would lead to poor monitoring results. Therefore, a Markov chain neighborhood sparse preserving graph embedding algorithm based on tensor factorization (TMNSPGE) is proposed. Firstly, tensor factorization is used to directly process the three-dimension data in batch process, which can avoid the information loss. Secondly, by using the neighborhood preserving embedding algorithm and sparse manifold coding, the local linear relationship and local sparse manifold structure of data are preserved. On this basis, Markov chain analysis is introduced to construct a similar graph to make the data after dimensionality reduction have a certain probability interpretation. Finally, the statistics and control limits are determined to realize process monitoring. Numerical example and penicillin fermentation simulation process prove the effectiveness of TMNSPGE algorithm in batch process monitoring. © 2013 IEEE.
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
WOS记录号	WOS:000613537400001
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/147221]
专题	电气工程与信息工程学院
作者单位	College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China Key Laboratory of Gansu Advanced Control for Industrial Process, Lanzhou University of Technology, Lanzhou 730050, China National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou 730050, China
推荐引用方式 GB/T 7714	Zhao, Xiaoqiang,Mou, Miao. Markov Chain Neighborhood Sparse Preserving Graph Embedding Based on Tensor Factorization for Batch Process Monitoring[J]. IEEE Access,2021,9:16211-16224.
APA	Zhao, Xiaoqiang,&Mou, Miao.(2021).Markov Chain Neighborhood Sparse Preserving Graph Embedding Based on Tensor Factorization for Batch Process Monitoring.IEEE Access,9,16211-16224.
MLA	Zhao, Xiaoqiang,et al."Markov Chain Neighborhood Sparse Preserving Graph Embedding Based on Tensor Factorization for Batch Process Monitoring".IEEE Access 9(2021):16211-16224.