CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Process Modeling and Monitoring With Incomplete Data Based on Robust Probabilistic Partial Least Square Method
Li, Qinghua1; Pan, Feng1; Zhao, Zhonggai1; Yu, Junzhi2
Source PublicationIEEE ACCESS
2018
Volume6Pages:10160-10168
SubtypeArticle
AbstractIn 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.
KeywordProcess Modeling Process Monitoring Robust Ppls Method Missing Data
WOS HeadingsScience & Technology ; Technology
DOI10.1109/ACCESS.2018.2810079
WOS KeywordMISSING DATA ; LATENT STRUCTURES ; PCA ; PREDICTION ; REGRESSION ; PROJECTION ; DIAGNOSIS
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(NSFC 61573169 ; NSFC 61725305)
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000427991400001
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21988
Collection复杂系统管理与控制国家重点实验室_先进机器人
Affiliation1.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
Recommended Citation
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.
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