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Volumn 66, Issue 21, 2011, Pages 5173-5183

Two-dimensional Bayesian monitoring method for nonlinear multimode processes

Author keywords

Linear subspace; Multimode; Nonlinear; Process monitoring; Two dimensional Bayesian inference; Two step variable selection

Indexed keywords

BAYESIAN INFERENCE; LINEAR SUBSPACE; MULTIMODES; NONLINEAR; VARIABLE SELECTION;

EID: 80051914224     PISSN: 00092509     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ces.2011.07.001     Document Type: Article
Times cited : (53)

References (43)
  • 1
    • 36749052831 scopus 로고    scopus 로고
    • Monitoring a complex refining process using multivariate statistics
    • AlGhazzawi A., Lennox B. Monitoring a complex refining process using multivariate statistics. Control Engineering Practice 2008, 16:294-307.
    • (2008) Control Engineering Practice , vol.16 , pp. 294-307
    • AlGhazzawi, A.1    Lennox, B.2
  • 2
    • 51649108757 scopus 로고    scopus 로고
    • Soft clustering using weighted one-class support vector machines
    • Bicego M., Figueiredo M.A.T. Soft clustering using weighted one-class support vector machines. Pattern recognition 2009, 42:27-32.
    • (2009) Pattern recognition , vol.42 , pp. 27-32
    • Bicego, M.1    Figueiredo, M.A.T.2
  • 4
    • 33645311553 scopus 로고    scopus 로고
    • Multi-phase principal component analysis for batch processes modeling
    • Camacho J., Pico J. Multi-phase principal component analysis for batch processes modeling. Chemometrics and Intelligent Laborator Syetems 2006, 81:127-136.
    • (2006) Chemometrics and Intelligent Laborator Syetems , vol.81 , pp. 127-136
    • Camacho, J.1    Pico, J.2
  • 5
    • 33644990573 scopus 로고    scopus 로고
    • On-line batch process monitoring using MHMT-based MPCA
    • Chen J.H., Chen H.H. On-line batch process monitoring using MHMT-based MPCA. Chemical Engineering Science 2006, 61:3223-3239.
    • (2006) Chemical Engineering Science , vol.61 , pp. 3223-3239
    • Chen, J.H.1    Chen, H.H.2
  • 6
    • 60249095677 scopus 로고    scopus 로고
    • Probabilistic contribution analysis for statistical process monitoring: a missing variable approach
    • Chen T., Sun Y. Probabilistic contribution analysis for statistical process monitoring: a missing variable approach. Control Engineering Practice 2009, 17:469-477.
    • (2009) Control Engineering Practice , vol.17 , pp. 469-477
    • Chen, T.1    Sun, Y.2
  • 7
    • 56349129392 scopus 로고    scopus 로고
    • A fuzzy c-means clustering-based fragile watermarking scheme for image authentication
    • Chen W.C., Wang M.S. A fuzzy c-means clustering-based fragile watermarking scheme for image authentication. Expert Systems with Applications 2009, 36:1300-1307.
    • (2009) Expert Systems with Applications , vol.36 , pp. 1300-1307
    • Chen, W.C.1    Wang, M.S.2
  • 9
    • 2342521341 scopus 로고    scopus 로고
    • Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis
    • Choi S.W., Park J.H., Lee I.B. Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis. Computers and Chemical Engineering 2004, 28:1377-1387.
    • (2004) Computers and Chemical Engineering , vol.28 , pp. 1377-1387
    • Choi, S.W.1    Park, J.H.2    Lee, I.B.3
  • 11
    • 0043015539 scopus 로고    scopus 로고
    • Nonlinear principal component analysis-based on principal curves and neural networks
    • Dong D., McAvoy T.J. Nonlinear principal component analysis-based on principal curves and neural networks. Computers and Chemical Engineering 1996, 20:65-78.
    • (1996) Computers and Chemical Engineering , vol.20 , pp. 65-78
    • Dong, D.1    McAvoy, T.J.2
  • 13
    • 34247109083 scopus 로고    scopus 로고
    • Process monitoring based on independent component analysis-principal component analysis (ICA-PCA) and similarity factors
    • Ge Z.Q., Song Z.H. Process monitoring based on independent component analysis-principal component analysis (ICA-PCA) and similarity factors. Industrial and Engineering Chemistry Research 2007, 46:2054-2063.
    • (2007) Industrial and Engineering Chemistry Research , vol.46 , pp. 2054-2063
    • Ge, Z.Q.1    Song, Z.H.2
  • 14
    • 50649095932 scopus 로고    scopus 로고
    • Online monitoring of nonlinear multiple mode processes based on adaptive local model approach
    • Ge Z.Q., Song Z.H. Online monitoring of nonlinear multiple mode processes based on adaptive local model approach. Control Engineering Practice 2008, 16:1427-1437.
    • (2008) Control Engineering Practice , vol.16 , pp. 1427-1437
    • Ge, Z.Q.1    Song, Z.H.2
  • 16
    • 63249084878 scopus 로고    scopus 로고
    • Improved kernel PCA-based monitoring approach for nonlinear processes
    • Ge Z.Q., Yang C.J., Song Z.H. Improved kernel PCA-based monitoring approach for nonlinear processes. Chemical Engineering Science 2009, 64:2245-2255.
    • (2009) Chemical Engineering Science , vol.64 , pp. 2245-2255
    • Ge, Z.Q.1    Yang, C.J.2    Song, Z.H.3
  • 17
    • 77955305868 scopus 로고    scopus 로고
    • Nonlinear process monitoring based on linear subspace and Bayesian inference
    • Ge Z.Q., Zhang M.G., Song Z.H. Nonlinear process monitoring based on linear subspace and Bayesian inference. Journal of Process Control 2010, 20:676-688.
    • (2010) Journal of Process Control , vol.20 , pp. 676-688
    • Ge, Z.Q.1    Zhang, M.G.2    Song, Z.H.3
  • 19
    • 44649092618 scopus 로고    scopus 로고
    • Multivariate statistical process control based on multiway locality preserving projections
    • Hu K.L., Yuan J.Q. Multivariate statistical process control based on multiway locality preserving projections. Journal of Process control 2008, 18:797-807.
    • (2008) Journal of Process control , vol.18 , pp. 797-807
    • Hu, K.L.1    Yuan, J.Q.2
  • 20
    • 0032686509 scopus 로고    scopus 로고
    • Real-time monitoring for a process with multiple operating modes
    • Hwang D.H., Han C. Real-time monitoring for a process with multiple operating modes. Control Engineering Practice 1999, 7:891-902.
    • (1999) Control Engineering Practice , vol.7 , pp. 891-902
    • Hwang, D.H.1    Han, C.2
  • 22
    • 36749015609 scopus 로고    scopus 로고
    • SubXPCA and a generalized feature partitioning approach to principal component analysis
    • Kumar K.V., Negi A. SubXPCA and a generalized feature partitioning approach to principal component analysis. Pattern Recognition 2008, 41:1398-1409.
    • (2008) Pattern Recognition , vol.41 , pp. 1398-1409
    • Kumar, K.V.1    Negi, A.2
  • 23
    • 1342285571 scopus 로고    scopus 로고
    • Statistical process monitoring with independent component analysis
    • Lee J.M., Yoo C.K., Lee I.B. Statistical process monitoring with independent component analysis. Journal of Process Control 2004, 14:467-485.
    • (2004) Journal of Process Control , vol.14 , pp. 467-485
    • Lee, J.M.1    Yoo, C.K.2    Lee, I.B.3
  • 25
    • 33646182626 scopus 로고    scopus 로고
    • A multi-scale orthogonal nonlinear strategy for multi-variate statistical process monitoring
    • Maulud A., Wang D., Romagnoli J.A. A multi-scale orthogonal nonlinear strategy for multi-variate statistical process monitoring. Journal of Process Control 2006, 16:671-683.
    • (2006) Journal of Process Control , vol.16 , pp. 671-683
    • Maulud, A.1    Wang, D.2    Romagnoli, J.A.3
  • 26
    • 77953535805 scopus 로고    scopus 로고
    • Multi-model based process condition monitoring of offshore oil and gas production process
    • Natarajan S., Srinivasan R. Multi-model based process condition monitoring of offshore oil and gas production process. Chemical Engineering Research and Design 2010, 88:572-591.
    • (2010) Chemical Engineering Research and Design , vol.88 , pp. 572-591
    • Natarajan, S.1    Srinivasan, R.2
  • 27
    • 67349090576 scopus 로고    scopus 로고
    • An adjoined multi-model approach for monitoring batch and transient operations
    • Ng Y.S., Srinivasan R. An adjoined multi-model approach for monitoring batch and transient operations. Computers and Chemical Engineering 2009, 33:887-902.
    • (2009) Computers and Chemical Engineering , vol.33 , pp. 887-902
    • Ng, Y.S.1    Srinivasan, R.2
  • 29
    • 0032044750 scopus 로고    scopus 로고
    • Recursive PLS algorithms for adaptive data monitoring
    • Qin S.J. Recursive PLS algorithms for adaptive data monitoring. Computers and Chemical Engineering 1998, 22:503-514.
    • (1998) Computers and Chemical Engineering , vol.22 , pp. 503-514
    • Qin, S.J.1
  • 30
    • 0242354134 scopus 로고    scopus 로고
    • Statistical process monitoring: basics and beyond
    • Qin S.J. Statistical process monitoring: basics and beyond. Journal of Chemometrics 2003, 17:480-502.
    • (2003) Journal of Chemometrics , vol.17 , pp. 480-502
    • Qin, S.J.1
  • 31
    • 0030217795 scopus 로고    scopus 로고
    • Decentralized control of the Tennessee Eastman challenge process
    • Ricker N.L. Decentralized control of the Tennessee Eastman challenge process. Journal of Process Control 1996, 6:205-221.
    • (1996) Journal of Process Control , vol.6 , pp. 205-221
    • Ricker, N.L.1
  • 33
    • 33645389475 scopus 로고    scopus 로고
    • Evaluation of a pattern matching method for the Tennessee Eastman challenge process
    • Singhai A., Seborg D.E. Evaluation of a pattern matching method for the Tennessee Eastman challenge process. Journal of Process Control 2006, 16:601-613.
    • (2006) Journal of Process Control , vol.16 , pp. 601-613
    • Singhai, A.1    Seborg, D.E.2
  • 34
    • 79953818960 scopus 로고    scopus 로고
    • A method for multiphase batch process monitoring based on auto phase identification
    • Sun W., Meng Y., Palazoglu A., Zhao J., Zhang H., Zhang J. A method for multiphase batch process monitoring based on auto phase identification. Journal of Process Control 2011, 21:627-638.
    • (2011) Journal of Process Control , vol.21 , pp. 627-638
    • Sun, W.1    Meng, Y.2    Palazoglu, A.3    Zhao, J.4    Zhang, H.5    Zhang, J.6
  • 36
    • 0033556788 scopus 로고    scopus 로고
    • Mixtures of probabilistic principal component analysers
    • Tipping M.E., Bishop C.M. Mixtures of probabilistic principal component analysers. Neural Computation 1999, 11:443-482.
    • (1999) Neural Computation , vol.11 , pp. 443-482
    • Tipping, M.E.1    Bishop, C.M.2
  • 39
    • 44749086556 scopus 로고    scopus 로고
    • Subspace identification for two-dimensional dynamic batch process statistical monitoring
    • Yao Y., Gao F. Subspace identification for two-dimensional dynamic batch process statistical monitoring. Chemical Engineering Science 2008, 63:3411-3418.
    • (2008) Chemical Engineering Science , vol.63 , pp. 3411-3418
    • Yao, Y.1    Gao, F.2
  • 41
    • 47549099484 scopus 로고    scopus 로고
    • Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models
    • Yu J., Qin S.J. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models. AIChE Journal 2008, 54:1811-1829.
    • (2008) AIChE Journal , vol.54 , pp. 1811-1829
    • Yu, J.1    Qin, S.J.2
  • 42
    • 57049177632 scopus 로고    scopus 로고
    • Improved nonlinear fault detection technique and statistical analysis
    • Zhang Y.W., Qin S.J. Improved nonlinear fault detection technique and statistical analysis. AIChE Journal 2008, 54:3207-3220.
    • (2008) AIChE Journal , vol.54 , pp. 3207-3220
    • Zhang, Y.W.1    Qin, S.J.2
  • 43
    • 6344249065 scopus 로고    scopus 로고
    • Monitoring of processes with multiple operation modes through multiple principle component analysis models
    • Zhao S.J., Zhang J., Xu Y.M. Monitoring of processes with multiple operation modes through multiple principle component analysis models. Industrial and Engineering Chemistry Research 2004, 43:7025-7035.
    • (2004) Industrial and Engineering Chemistry Research , vol.43 , pp. 7025-7035
    • Zhao, S.J.1    Zhang, J.2    Xu, Y.M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.