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Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set
Li JN(李金娜); Li Y(李元); Yu HB(于海斌); Xie YH(谢彦红); Zhang C(张成)
Department工业控制网络与系统研究室
Source PublicationJOURNAL OF APPLIED MATHEMATICS
ISSN1110-757X
2012
Volume2012Pages:1-17
Indexed BySCI
WOS IDWOS:000310301500001
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China [61174119, 61104093, 61034006, 61174026]; MOST [2010CB334705]; National High Technology Research and Development Program of China (863 Program) [2011AA040101]; Liaoning Province of China [L2012141, L2011064]
KeywordPrincipal Component Analysis Observer Systems Diagnosis Fuzzy Identification Pca
AbstractA novel fault detection technique is proposed to explicitly account for the nonlinear, dynamic, and multimodal problems existed in the practical and complex dynamic processes. Just-in-time (JIT) detection method and k-nearest neighbor (KNN) rule-based statistical process control (SPC) approach are integrated to construct a flexible and adaptive detection scheme for the control process with nonlinear, dynamic, and multimodal cases. Mahalanobis distance, representing the correlation among samples, is used to simplify and update the raw data set, which is the first merit in this paper. Based on it, the control limit is computed in terms of both KNN rule and SPC method, such that we can identify whether the current data is normal or not by online approach. Noted that the control limit obtained changes with updating database such that an adaptive fault detection technique that can effectively eliminate the impact of data drift and shift on the performance of detection process is obtained, which is the second merit in this paper. The efficiency of the developed method is demonstrated by the numerical examples and an industrial case.
Language英语
WOS HeadingsScience & Technology ; Physical Sciences
WOS SubjectMathematics, Applied
WOS KeywordPRINCIPAL COMPONENT ANALYSIS ; NEAREST-NEIGHBOR RULE ; SYSTEM IDENTIFICATION ; DIAGNOSIS ; OBSERVER ; FUZZY ; PCA
WOS Research AreaMathematics
Citation statistics
Cited Times:50[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/12528
Collection工业控制网络与系统研究室
Corresponding AuthorLi JN(李金娜)
Affiliation1.Department of Science, Shenyang University of Chemical Technology, Liaoning, Shenyang 110142, China
2.Lab of Industrial Control Networks and Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Liaoning, Shenyang 110016, China
3.College of Information Engineering, Shenyang University of Chemical Technology, Liaoning, Shenyang 110142, China
Recommended Citation
GB/T 7714
Li JN,Li Y,Yu HB,et al. Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set[J]. JOURNAL OF APPLIED MATHEMATICS,2012,2012:1-17.
APA Li JN,Li Y,Yu HB,Xie YH,&Zhang C.(2012).Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set.JOURNAL OF APPLIED MATHEMATICS,2012,1-17.
MLA Li JN,et al."Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set".JOURNAL OF APPLIED MATHEMATICS 2012(2012):1-17.
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