Process Modeling and Monitoring With Incomplete Data Based on Robust Probabilistic Partial Least Square Method

doi:10.1109/ACCESS.2018.2810079

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进机器人控制团队

	Process Modeling and Monitoring With Incomplete Data Based on Robust Probabilistic Partial Least Square Method
	Li, Qinghua 1; Pan, Feng 1; Zhao, Zhonggai 1; Yu, Junzhi2
刊名	IEEE ACCESS
	2018
卷号	6 页码:10160-10168
关键词	Process Modeling Process Monitoring Robust Ppls Method Missing Data
DOI	10.1109/ACCESS.2018.2810079
文献子类	Article
英文摘要	In real industrial processes, both outliers and missing data are very common. Owing to the assumption that the data sampled from a normal process follow the Gaussian distribution, the regular data-driven process monitoring methods, such as the probabilistic partial least square (PPLS) method and the probabilistic principal component analysis method, are sensitive to outliers. By introducing heavy-tailed t distribution instead of Gaussian distribution to capture the distribution of normal data, the robust data-driven method can significantly reduce the influence of outliers on the development of the model. To reduce the influence of missing data, this paper proposes a process modeling and monitoring method with incomplete data based on the robust PPLS method. In the proposed method, to use more useful information in modeling, incomplete data along with complete data are employed in the parameter estimation using the maximum likelihood method; according to the robust PPLS model and the Bayes' rule, the distributions of latent variables and missing data are derived, and subsequently, the expectation-maximization algorithm is used to achieve the parameter estimation. In addition, based on the conditional distribution of missing data, two monitoring indices are developed to evaluate the deviation of latent variables and residuals. A simulation case illustrates the application of the proposed method, and the results of application demonstrate its efficacy.
WOS关键词	MISSING DATA ; LATENT STRUCTURES ; PCA ; PREDICTION ; REGRESSION ; PROJECTION ; DIAGNOSIS
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
WOS记录号	WOS:000427991400001
资助机构	National Natural Science Foundation of China(NSFC 61573169 ; NSFC 61725305)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/21988]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
作者单位	1.Jiangnan Univ, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Li, Qinghua,Pan, Feng,Zhao, Zhonggai,et al. Process Modeling and Monitoring With Incomplete Data Based on Robust Probabilistic Partial Least Square Method[J]. IEEE ACCESS,2018,6:10160-10168.
APA	Li, Qinghua,Pan, Feng,Zhao, Zhonggai,&Yu, Junzhi.(2018).Process Modeling and Monitoring With Incomplete Data Based on Robust Probabilistic Partial Least Square Method.IEEE ACCESS,6,10160-10168.
MLA	Li, Qinghua,et al."Process Modeling and Monitoring With Incomplete Data Based on Robust Probabilistic Partial Least Square Method".IEEE ACCESS 6(2018):10160-10168.