다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기
Chemical engineering science, v.68 no.1, 2012년, pp.506 - 519
Yu, J.
AI-Helper
A nonlinear kernel Gaussian mixture model (NKGMM) based inferential monitoring method is proposed in this article for chemical process fault detection and diagnosis. Aimed at the multimode non-Gaussian process with within-mode nonlinearity, the developed NKGMM approach projects the operating data fr...
Ind. Eng. Chem. Res. Albazzaz 43 6731 2004 10.1021/ie049582+ Statistical process control charts for batch operations based on independent component analysis
AIChE J. Bakshi 44 7 1596 1998 10.1002/aic.690440712 Multiscale PCA with application to multivariate statistical process monitoring
Bishop 1995 Neural Networks for Pattern Recognition
10.1007/978-1-4471-0347-9 Chiang, L., Russell, E., Braatz, R., 2001. Fault Detection and Diagnosis in Industrial Systems. Advanced Textbooks in Control and Signal Processing. Springer-Verlag, London, Great Britain.
Comput. Chem. Eng. Chiang 28 1389 2004 10.1016/j.compchemeng.2003.10.002 Fault diagnosis based on Fisher discriminant analysis and support vector machines
Duda 2001 Pattern Classification
IEEE Trans. Pattern Anal. Mach. Intell. Figueiredo 24 381 2002 10.1109/34.990138 Unsupervised learning of finite mixture models
Control Eng. Practice Hwang 7 891 1999 10.1016/S0967-0661(99)00038-6 Real-time monitoring for a process with multiple operating modes
AIChE J. Kano 49 4 969 2003 10.1002/aic.690490414 Monitoring independent components for fault detection
Chem. Eng. Sci. Lee 59 223 2004 10.1016/j.ces.2003.09.012 Nonlinear process monitoring using kernel principal component analysis
J. Process Control Lee 14 467 2004 10.1016/j.jprocont.2003.09.004 Statistical process monitoring with independent component analysis
Chem. Eng. Sci. Lee 59 223 2004 10.1016/j.ces.2003.09.012 Nonlinear process monitoring using kernel principal component analysis
AIChE J. Lee 52 3501 2006 10.1002/aic.10978 Fault detection and diagnosis based on modified independent component analysis
Biotechnol. Bioeng. Lennox 74 125 2001 10.1002/bit.1102 Process monitoring of an industrial fed-batch fermentation
AIChE J. Lu 50 255 2004 10.1002/aic.10024 Sub-PCA modeling and on-line monitoring strategy for batch processes
Control Eng. Practice MacGregor 3 3 403 1995 10.1016/0967-0661(95)00014-L Statistical process control of multivariate processes
AIChE J. Nomikos 40 1361 1994 10.1002/aic.690400809 Monitoring of batch processes using multi-way principal component analysis
IEEE Control Systems Mag. Piovoso 22 8 2002 10.1109/MCS.2002.1035213 Multivariate statistics for process control
J. Chemomet. Qin 17 480 2003 10.1002/cem.800 Statistical process monitoring: basics and beyond
J. Process Control Qin 17 221 2007 10.1016/j.jprocont.2006.11.002 Recent developments in multivariable controller performance monitoring
Vapnik 2000 The Nature of Statistical Learning Theory
Lecture Notes in Computer Science Wang 2842 159 2003 10.1007/978-3-540-39624-6_14 Kernel trick embedded Gaussian mixture model
J. Process Control Wise 6 329 1996 10.1016/0959-1524(96)00009-1 The process chemometrics approach to process monitoring and fault detection
J. Chemomet. Wold 10 463 1996 10.1002/(SICI)1099-128X(199609)10:5/6<463::AID-CEM445>3.0.CO;2-L Hierarchical multiblock PLS and PC models for easier model interpretation and as an alternative to variable selection
Comput. Chem. Eng. Yelamos 33 244 2009 10.1016/j.compchemeng.2008.08.008 Performance assessment of a novel fault diagnosis system based on support vector machines
AIChE J. Yu 57 1817 2011 10.1002/aic.12392 Localized Fisher discriminant analysis based complex chemical process monitoring
Ind. Eng. Chem. Res. Yu 50 3390 2011 10.1021/ie1017282 Nonlinear bioprocess monitoring using multiway kernel localized Fisher discriminant analysis
J. Process Control Yu 18 277 2008 10.1016/j.jprocont.2007.06.002 Statistical MIMO controller performance monitoring. Part I: data driven covariance benchmark
J. Process Control Yu 18 297 2008 10.1016/j.jprocont.2007.09.003 Statistical MIMO controller performance monitoring. Part II: performance diagnosis
AIChE J. Yu 54 1811 2008 10.1002/aic.11515 Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models
IIE Trans. Yu 41 764 2009 10.1080/07408170902789076 Variance component analysis based fault diagnosis of multi-layer overlay lithography processes
Ind. Eng. Chem. Res. Yu 48 8585 2009 10.1021/ie900479g Multiway Gaussian mixture model based multiphase batch process monitoring
Chem. Eng. Sci. Zhang 64 801 2009 10.1016/j.ces.2008.10.012 Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
Copyright KISTI. All Rights Reserved.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.