메뉴 건너뛰기




Volumn 22, Issue 1, 2014, Pages 194-204

Modeling and monitoring of nonlinear multi-mode processes

Author keywords

Monitoring; Multi mode processes modeling; Subspace separation

Indexed keywords

COMPLEX INDUSTRY PROCESS; INTEGRATED MONITORING SYSTEMS; MONITORING APPROACH; MONITORING MODELS; MONITORING PERFORMANCE; MULTI-MODE PROCESS; SUBSPACE SEPARATION METHODS; SUBSPACE SEPARATIONS;

EID: 84889604438     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conengprac.2013.04.007     Document Type: Article
Times cited : (48)

References (38)
  • 1
    • 67349245154 scopus 로고    scopus 로고
    • Reconstruction-based contribution for process monitoring
    • Alcala C.F., Qin J.S. Reconstruction-based contribution for process monitoring. Automatica 2009, 45:1593-1600.
    • (2009) Automatica , vol.45 , pp. 1593-1600
    • Alcala, C.F.1    Qin, J.S.2
  • 2
    • 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. Industrial & Engineering Chemistry Research 2006, 81:127-136.
    • (2006) Industrial & Engineering Chemistry Research , vol.81 , pp. 127-136
    • Camacho, J.1    Pico, J.2
  • 3
    • 40949103011 scopus 로고    scopus 로고
    • Nonlinear multiscale modeling for fault detection and identification
    • Choi S.W., Morris J., Lee I.-B. Nonlinear multiscale modeling for fault detection and identification. Chemical Engineering Science 2008, 63:2252-2266.
    • (2008) Chemical Engineering Science , vol.63 , pp. 2252-2266
    • Choi, S.W.1    Morris, J.2    Lee, I.-B.3
  • 4
    • 0032144398 scopus 로고    scopus 로고
    • Subspace approach to multidimensional fault identification and reconstruction
    • Dunia R., Qin S.J. Subspace approach to multidimensional fault identification and reconstruction. AIChE Journal 1998, 44:1813-1831.
    • (1998) AIChE Journal , vol.44 , pp. 1813-1831
    • Dunia, R.1    Qin, S.J.2
  • 5
    • 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(12):1427-1437.
    • (2008) Control Engineering Practice , vol.16 , Issue.12 , pp. 1427-1437
    • Ge, Z.Q.1    Song, Z.H.2
  • 8
    • 67349245479 scopus 로고    scopus 로고
    • Comprehensive methodology for detection and diagnosis of oscillatory control loops
    • Karra S., Karim M. Comprehensive methodology for detection and diagnosis of oscillatory control loops. Control Engineering Practice 2009, 17(8):939-956.
    • (2009) Control Engineering Practice , vol.17 , Issue.8 , pp. 939-956
    • Karra, S.1    Karim, M.2
  • 10
    • 72149111135 scopus 로고    scopus 로고
    • Fault detection and diagnosis in process data using one-class support vector machines
    • Mahadevan S., Shah L.S. Fault detection and diagnosis in process data using one-class support vector machines. Journal of Process Control 2009, 19:1627-1639.
    • (2009) Journal of Process Control , vol.19 , pp. 1627-1639
    • Mahadevan, S.1    Shah, L.S.2
  • 11
    • 67349251537 scopus 로고    scopus 로고
    • Real time phase detection based online monitoring of batch fermentation processes
    • Maiti S.K., Srivastava R.K., Bhushan M., Wangikar P. Real time phase detection based online monitoring of batch fermentation processes. Process Biochemistry 2009, 44:799-811.
    • (2009) Process Biochemistry , vol.44 , pp. 799-811
    • Maiti, S.K.1    Srivastava, R.K.2    Bhushan, M.3    Wangikar, P.4
  • 12
    • 84898970836 scopus 로고    scopus 로고
    • Kernel PCA and de-noising in feature spaces. In Proceedings of the advances in neural information processing systems II
    • Mika, S., Schölkopf, B., Smola, A., Müller, K.-R., Scholz, M., & Rätsch, G. (1999). Kernel PCA and de-noising in feature spaces. In Proceedings of the advances in neural information processing systems II (pp. 536-542).
    • (1999) , pp. 536-542
    • Mika, S.1    Schölkopf, B.2    Smola, A.3    Müller, K.-R.4    Scholz, M.5    Rätsch, G.6
  • 13
    • 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
  • 14
    • 0001282938 scopus 로고    scopus 로고
    • On unifying multiblock analysis with application to decentralized process monitoring
    • Qin S.J., Valle S., Piovoso M.J. On unifying multiblock analysis with application to decentralized process monitoring. Journal of Chemometrics 2001, 15(9):715-742.
    • (2001) Journal of Chemometrics , vol.15 , Issue.9 , pp. 715-742
    • Qin, S.J.1    Valle, S.2    Piovoso, M.J.3
  • 15
    • 84861191986 scopus 로고    scopus 로고
    • A novel dissimilarity method by integrating multivariate mutual information and independent component analysis for non-Gaussian dynamic process monitoring
    • Rashid M.M., Yu J. A novel dissimilarity method by integrating multivariate mutual information and independent component analysis for non-Gaussian dynamic process monitoring. Chemometrics and Intelligent Laboratory Systems 2012, 115:44-58.
    • (2012) Chemometrics and Intelligent Laboratory Systems , vol.115 , pp. 44-58
    • Rashid, M.M.1    Yu, J.2
  • 16
    • 84859903438 scopus 로고    scopus 로고
    • Hidden Markov model based adaptive independent component analysis for chemical process monitoring
    • Rashid M.M., Yu J. Hidden Markov model based adaptive independent component analysis for chemical process monitoring. Industrial & Engineering Chemistry Research 2012, 51(15):5506-5514.
    • (2012) Industrial & Engineering Chemistry Research , vol.51 , Issue.15 , pp. 5506-5514
    • Rashid, M.M.1    Yu, J.2
  • 17
    • 34250887501 scopus 로고    scopus 로고
    • Multivariate statistical analysis of a multi-step industrial processes
    • Reinikainen S., Hoskuldsson A. Multivariate statistical analysis of a multi-step industrial processes. Analytica Chimica Acta 2007, 595(1-2):248-256.
    • (2007) Analytica Chimica Acta , vol.595 , Issue.1-2 , pp. 248-256
    • Reinikainen, S.1    Hoskuldsson, A.2
  • 18
    • 28844433766 scopus 로고    scopus 로고
    • PLS regression, PLS path modeling and generalized Procrustean analysis: A combined approach for multiblock analysis
    • Tenenhaus M., Vinzi V.E. PLS regression, PLS path modeling and generalized Procrustean analysis: A combined approach for multiblock analysis. Journal of Chemometrics 2005, 19(13):145-153.
    • (2005) Journal of Chemometrics , vol.19 , Issue.13 , pp. 145-153
    • Tenenhaus, M.1    Vinzi, V.E.2
  • 19
    • 0036805179 scopus 로고    scopus 로고
    • Statistical monitoring of multistage, multiphase batch processes
    • Undey C., Cinar A. Statistical monitoring of multistage, multiphase batch processes. IEEE Control Systems 2002, 22:40-52.
    • (2002) IEEE Control Systems , vol.22 , pp. 40-52
    • Undey, C.1    Cinar, A.2
  • 20
    • 0141566609 scopus 로고    scopus 로고
    • Online batch/fed-batch process performance monitoring, quality prediction, and variable-contribution analysis for diagnosis
    • Undey C., Ertunc S., Cinar A. Online batch/fed-batch process performance monitoring, quality prediction, and variable-contribution analysis for diagnosis. Industrial & Engineering Chemistry Research 2003, 42:4645-4658.
    • (2003) Industrial & Engineering Chemistry Research , vol.42 , pp. 4645-4658
    • Undey, C.1    Ertunc, S.2    Cinar, A.3
  • 21
    • 83655201159 scopus 로고    scopus 로고
    • Process monitoring based on mode identification for multi-mode process with transitions
    • Wang F.L., Tan S., Peng J. Process monitoring based on mode identification for multi-mode process with transitions. Chemometrics and Intelligent Laboratory Systems 2012, 110(1):144-155.
    • (2012) Chemometrics and Intelligent Laboratory Systems , vol.110 , Issue.1 , pp. 144-155
    • Wang, F.L.1    Tan, S.2    Peng, J.3
  • 22
    • 0033330731 scopus 로고    scopus 로고
    • Cycle-to-cycle and within-cycle adaptive control of nozzle pressures during packing-holding for thermoplastic injection molding
    • Yang Y., Gao F.R. Cycle-to-cycle and within-cycle adaptive control of nozzle pressures during packing-holding for thermoplastic injection molding. Polymer Engineering and Science 1999, 39:2042-2064.
    • (1999) Polymer Engineering and Science , vol.39 , pp. 2042-2064
    • Yang, Y.1    Gao, F.R.2
  • 23
    • 0034332656 scopus 로고    scopus 로고
    • Adaptive control of the filling velocity of thermoplastics injection molding
    • Yang Y., Gao F.R. Adaptive control of the filling velocity of thermoplastics injection molding. Control Engineering Practice 2000, 8:1285-1296.
    • (2000) Control Engineering Practice , vol.8 , pp. 1285-1296
    • Yang, Y.1    Gao, F.R.2
  • 24
    • 79958703777 scopus 로고    scopus 로고
    • Localized Fisher discriminant analysis based complex chemical process monitoring
    • Yu J. Localized Fisher discriminant analysis based complex chemical process monitoring. AIChE Journal 2011, 57(7):1817-1828.
    • (2011) AIChE Journal , vol.57 , Issue.7 , pp. 1817-1828
    • Yu, J.1
  • 25
    • 81055156706 scopus 로고    scopus 로고
    • A Nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes
    • Yu J. A Nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes. Chemical Engineering Science 2012, 68(1):506-519.
    • (2012) Chemical Engineering Science , vol.68 , Issue.1 , pp. 506-519
    • Yu, J.1
  • 26
    • 84862799924 scopus 로고    scopus 로고
    • A particle filter driven dynamic Gaussian mixture model approach for complex process monitoring and fault diagnosis
    • Yu J. A particle filter driven dynamic Gaussian mixture model approach for complex process monitoring and fault diagnosis. Journal of Process Control 2012, 22(4):778-788.
    • (2012) Journal of Process Control , vol.22 , Issue.4 , pp. 778-788
    • Yu, J.1
  • 27
    • 84859392648 scopus 로고    scopus 로고
    • A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses
    • Yu J. A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses. Computers and Chemical Engineering 2012, 41:134-144.
    • (2012) Computers and Chemical Engineering , vol.41 , pp. 134-144
    • Yu, J.1
  • 28
    • 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(7):1811-1829.
    • (2008) AIChE Journal , vol.54 , Issue.7 , pp. 1811-1829
    • Yu, J.1    Qin, S.J.2
  • 29
    • 58749115727 scopus 로고    scopus 로고
    • Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM
    • Zhang Y.W. Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM. Chemical Engineering Science 2009, 64(5):801-811.
    • (2009) Chemical Engineering Science , vol.64 , Issue.5 , pp. 801-811
    • Zhang, Y.W.1
  • 31
    • 84863151045 scopus 로고    scopus 로고
    • Dynamic processes monitoring using recursive kernel principal component analysis
    • Zhang Y.W., Li S., Teng Y.D. Dynamic processes monitoring using recursive kernel principal component analysis. Chemical Engineering Science 2012, 72(1):78-86.
    • (2012) Chemical Engineering Science , vol.72 , Issue.1 , pp. 78-86
    • Zhang, Y.W.1    Li, S.2    Teng, Y.D.3
  • 32
    • 78149468553 scopus 로고    scopus 로고
    • Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS
    • Zhang Y.W., Ma C. Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS. Chemical Engineering Science 2011, 66(1):64-72.
    • (2011) Chemical Engineering Science , vol.66 , Issue.1 , pp. 64-72
    • Zhang, Y.W.1    Ma, C.2
  • 33
    • 57049177632 scopus 로고    scopus 로고
    • Improved nonlinear fault detection technology and statistical analysis
    • Zhang Y.W., Qin S.J. Improved nonlinear fault detection technology and statistical analysis. AIChE Journal 2008, 54(12):3207-3220.
    • (2008) AIChE Journal , vol.54 , Issue.12 , pp. 3207-3220
    • Zhang, Y.W.1    Qin, S.J.2
  • 34
    • 77954386508 scopus 로고    scopus 로고
    • Fault detection of non-Gaussian processes based on modified independent component analysis
    • Zhang Y.W., Zhang Y. Fault detection of non-Gaussian processes based on modified independent component analysis. Chemical Engineering Science 2010, 65(16):4630-4639.
    • (2010) Chemical Engineering Science , vol.65 , Issue.16 , pp. 4630-4639
    • Zhang, Y.W.1    Zhang, Y.2
  • 35
    • 79960276964 scopus 로고    scopus 로고
    • Fault magnitude estimation for processes
    • Zhang Y.W., Zhang Y., Li C. Fault magnitude estimation for processes. Chemical Engineering Science 2011, 66(18):4261-4267.
    • (2011) Chemical Engineering Science , vol.66 , Issue.18 , pp. 4261-4267
    • Zhang, Y.W.1    Zhang, Y.2    Li, C.3
  • 36
    • 76849100172 scopus 로고    scopus 로고
    • Decentralized fault diagnosis of large-scale processes using multiblock kernel partial least squares
    • Zhang Y.W., Zhou H., Qin S.J., Chai T.Y. Decentralized fault diagnosis of large-scale processes using multiblock kernel partial least squares. IEEE Transactions on Industrial Informatics 2010, 6(1):3-12.
    • (2010) IEEE Transactions on Industrial Informatics , vol.6 , Issue.1 , pp. 3-12
    • Zhang, Y.W.1    Zhou, H.2    Qin, S.J.3    Chai, T.Y.4
  • 37
    • 53649106193 scopus 로고    scopus 로고
    • Nonlinear process monitoring based on kernel dissimilarity analysis
    • Zhao C.H., Wang F.L., Zhang Y.W. Nonlinear process monitoring based on kernel dissimilarity analysis. Control Engineering Practice 2009, 17(1):221-230.
    • (2009) Control Engineering Practice , vol.17 , Issue.1 , pp. 221-230
    • Zhao, C.H.1    Wang, F.L.2    Zhang, Y.W.3
  • 38
    • 77957599856 scopus 로고    scopus 로고
    • Total projection to latent structures for process monitoring
    • Zhou D.H., Li G., Qin S.J. Total projection to latent structures for process monitoring. AIChE Journal 2010, 56(1):168-178.
    • (2010) AIChE Journal , vol.56 , Issue.1 , pp. 168-178
    • Zhou, D.H.1    Li, G.2    Qin, S.J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.