메뉴 건너뛰기




Volumn 58, Issue 8, 2012, Pages 2357-2372

Local ICA for multivariate statistical fault diagnosis in systems with unknown signal and error distributions

Author keywords

Fault reconstruction; MLPCA; Model plane; Non Gaussian signals; Statistical local approach

Indexed keywords

ABNORMAL CONDITIONS; APPLICATION STUDIES; ERROR DISTRIBUTIONS; FAULT RECONSTRUCTION; GAUSSIANS; INCIPIENT FAULTS; LOCAL APPROACHES; MLPCA; NONGAUSSIAN SIGNALS; OPERATING CONDITION; SIMULATION EXAMPLE;

EID: 84863563165     PISSN: 00011541     EISSN: 15475905     Source Type: Journal    
DOI: 10.1002/aic.12760     Document Type: Article
Times cited : (57)

References (28)
  • 1
    • 0038959172 scopus 로고    scopus 로고
    • Probabilistic principal component analysis
    • Tipping ME, Bishop CM. Probabilistic principal component analysis. J R Stat Soc Series B. 1999; 61: 611-622.
    • (1999) J R Stat Soc Series B , vol.61 , pp. 611-622
    • Tipping, M.E.1    Bishop, C.M.2
  • 2
    • 35648957812 scopus 로고    scopus 로고
    • Model identification and error covariance matrix estimation from noisy data using PCA
    • Narasimhan S, Shah SL. Model identification and error covariance matrix estimation from noisy data using PCA. Control Eng Pract. 2008; 16: 146-155.
    • (2008) Control Eng Pract , vol.16 , pp. 146-155
    • Narasimhan, S.1    Shah, S.L.2
  • 3
    • 52649119206 scopus 로고    scopus 로고
    • Statistical-based monitoring of multivariate non-Gaussian systems
    • Liu X, Xie L, Kruger U, Littler T, Wang S. Statistical-based monitoring of multivariate non-Gaussian systems. AIChE J. 2008; 54: 2379-2391.
    • (2008) AIChE J , vol.54 , pp. 2379-2391
    • Liu, X.1    Xie, L.2    Kruger, U.3    Littler, T.4    Wang, S.5
  • 4
    • 78650303983 scopus 로고    scopus 로고
    • A unified statistical framework for monitoring multivariate systems with un-known source and error signals
    • Feital T, Kruger U, Xie L, Schubert U, Lima EL, Pinto JC. A unified statistical framework for monitoring multivariate systems with un-known source and error signals. Chemometr Intell Lab Syst. 2010; 104: 223-232.
    • (2010) Chemometr Intell Lab Syst , vol.104 , pp. 223-232
    • Feital, T.1    Kruger, U.2    Xie, L.3    Schubert, U.4    Lima, E.L.5    Pinto, J.C.6
  • 5
    • 1942468131 scopus 로고    scopus 로고
    • Evolution of multivariate statistical process control: application of independent component analysis and external analysis
    • Kano M, Hasebe S, Hashimoto I, Ohno H. Evolution of multivariate statistical process control: application of independent component analysis and external analysis. Comp Chem Eng. 2004; 28: 1157-1166.
    • (2004) Comp Chem Eng , vol.28 , pp. 1157-1166
    • Kano, M.1    Hasebe, S.2    Hashimoto, I.3    Ohno, H.4
  • 6
    • 1342285571 scopus 로고    scopus 로고
    • Statistical process monitoring with independent component analysis
    • Lee JM, Yoo CK, Lee IB. Statistical process monitoring with independent component analysis. J Process Contr. 2004; 14: 467-485.
    • (2004) J Process Contr , vol.14 , pp. 467-485
    • Lee, J.M.1    Yoo, C.K.2    Lee, I.B.3
  • 7
    • 33749473097 scopus 로고    scopus 로고
    • Fault detection and diagnosis based on modified independent component analysis
    • Lee JM, Qin SJ, Lee IB. Fault detection and diagnosis based on modified independent component analysis. AIChE J. 2006; 52: 3501-3514.
    • (2006) AIChE J , vol.52 , pp. 3501-3514
    • Lee, J.M.1    Qin, S.J.2    Lee, I.B.3
  • 8
    • 1142268899 scopus 로고    scopus 로고
    • Regularised kernel density estimation for clustered process data
    • Chen Q, Kruger U, Leung AYT. Regularised kernel density estimation for clustered process data. Control Eng Pract. 2004; 12: 267-274.
    • (2004) Control Eng Pract , vol.12 , pp. 267-274
    • Chen, Q.1    Kruger, U.2    Leung, A.Y.T.3
  • 9
    • 71849088402 scopus 로고    scopus 로고
    • Sensor fault identification and isolation for mutltivariate non-Gaussian processes
    • Ge Z, Xie L, Kruger U, Lamont L, Song Z, Wang S. Sensor fault identification and isolation for mutltivariate non-Gaussian processes. J Process Control. 2009; 19: 1707-1715.
    • (2009) J Process Control , vol.19 , pp. 1707-1715
    • Ge, Z.1    Xie, L.2    Kruger, U.3    Lamont, L.4    Song, Z.5    Wang, S.6
  • 10
    • 0032204039 scopus 로고    scopus 로고
    • On-board component fault detection and isolation using the statistical local approach
    • Basseville M. On-board component fault detection and isolation using the statistical local approach. Automatica. 1998; 34: 1391-1415.
    • (1998) Automatica , vol.34 , pp. 1391-1415
    • Basseville, M.1
  • 11
    • 54949117106 scopus 로고    scopus 로고
    • Diagnosis of process faults in chemical systems using the local partial least squares approach
    • Kruger U, Dimitriadis D. Diagnosis of process faults in chemical systems using the local partial least squares approach. AIChE J. 2008; 54: 2581-2596.
    • (2008) AIChE J , vol.54 , pp. 2581-2596
    • Kruger, U.1    Dimitriadis, D.2
  • 12
    • 0032144398 scopus 로고    scopus 로고
    • Subspace approach for to multidimensional fault identification and reconstruction
    • Dunia R, Qin SJ. Subspace approach for to multidimensional fault identification and reconstruction. AIChE J. 1998; 44: 2797-2812.
    • (1998) AIChE J , vol.44 , pp. 2797-2812
    • Dunia, R.1    Qin, S.J.2
  • 13
    • 33645880467 scopus 로고    scopus 로고
    • Improved reliability in diagnosing faults using multivariate statistics
    • Lieftucht D, Kruger U, G.W. Irwin Improved reliability in diagnosing faults using multivariate statistics. Comp Chem Eng. 2006; 30: 901-912.
    • (2006) Comp Chem Eng , vol.30 , pp. 901-912
    • Lieftucht, D.1    Kruger, U.2    Irwin, G.W.3
  • 14
    • 84867057936 scopus 로고
    • Cumulative sum control charting: an underutilized SPC tool
    • Hawkins DM. Cumulative sum control charting: an underutilized SPC tool. Qual Eng. 1993; 5: 463-477.
    • (1993) Qual Eng , vol.5 , pp. 463-477
    • Hawkins, D.M.1
  • 16
    • 0041324208 scopus 로고    scopus 로고
    • Gaussian moments for noisy independent component analysis
    • Hyvarinen A. Gaussian moments for noisy independent component analysis. IEEE Signal Process Lett. 1999; 6: 145-147.
    • (1999) IEEE Signal Process Lett , vol.6 , pp. 145-147
    • Hyvarinen, A.1
  • 17
    • 53049106679 scopus 로고    scopus 로고
    • Gaussian moments for noisy unifying model
    • Yang YM, Guo CH. Gaussian moments for noisy unifying model. Neurocomputing. 2008; 71: 3656-3659.
    • (2008) Neurocomputing , vol.71 , pp. 3656-3659
    • Yang, Y.M.1    Guo, C.H.2
  • 18
    • 0042826822 scopus 로고    scopus 로고
    • Independent component analysis: algorithms and applications
    • Hyvarinen A, Oja E. Independent component analysis: algorithms and applications. Neural Network. 2000; 13: 411-430.
    • (2000) Neural Network , vol.13 , pp. 411-430
    • Hyvarinen, A.1    Oja, E.2
  • 19
    • 0001925804 scopus 로고
    • Multivariate control charts for individual observations
    • Tracey ND, Young JC, Mason RL. Multivariate control charts for individual observations. J Qual Technol. 1992; 24: 88-95.
    • (1992) J Qual Technol , vol.24 , pp. 88-95
    • Tracey, N.D.1    Young, J.C.2    Mason, R.L.3
  • 20
    • 53849142606 scopus 로고    scopus 로고
    • Regression approaches to small sample inverse covariance matrix estimation for hyperspectral image classification
    • Jensen AC, Berge A, Solberg AS. Regression approaches to small sample inverse covariance matrix estimation for hyperspectral image classification. IEEE Trans Geosci Remote Sens. 2008; 46: 2814-2822.
    • (2008) IEEE Trans Geosci Remote Sens , vol.46 , pp. 2814-2822
    • Jensen, A.C.1    Berge, A.2    Solberg, A.S.3
  • 21
    • 58149310770 scopus 로고    scopus 로고
    • Automatic PCA dimension selection for high dimensional data and small sample sizes
    • D.C. Hoyle Automatic PCA dimension selection for high dimensional data and small sample sizes. J Mac Learn Res. 2008; 9: 2733-2759.
    • (2008) J Mac Learn Res , vol.9 , pp. 2733-2759
    • Hoyle, D.C.1
  • 22
    • 34547567795 scopus 로고    scopus 로고
    • Improved principal component monitoring using the local approach
    • Kruger U, Kumar S, Littler T. Improved principal component monitoring using the local approach. Automatica. 2007; 43: 1532-1542.
    • (2007) Automatica , vol.43 , pp. 1532-1542
    • Kruger, U.1    Kumar, S.2    Littler, T.3
  • 23
    • 0035802262 scopus 로고    scopus 로고
    • Reconstruction-based fault identification using a combined index
    • Yue HH, Qin SJ. Reconstruction-based fault identification using a combined index. Ind Eng Chem Res. 2001; 40: 4403-4414.
    • (2001) Ind Eng Chem Res , vol.40 , pp. 4403-4414
    • Yue, H.H.1    Qin, S.J.2
  • 25
    • 0942266514 scopus 로고    scopus 로고
    • Support vector data description
    • Tax DMJ, Duin RPW. Support vector data description. Mac Learn. 2004; 54: 45-66.
    • (2004) Mac Learn , vol.54 , pp. 45-66
    • Tax, D.M.J.1    Duin, R.P.W.2
  • 26
    • 0018503842 scopus 로고
    • Control procedures for residuals associated with principal component analysis
    • Jackson JE, Mudholkar G. Control procedures for residuals associated with principal component analysis. Technometrics. 1979; 21: 341-349.
    • (1979) Technometrics , vol.21 , pp. 341-349
    • Jackson, J.E.1    Mudholkar, G.2
  • 27
    • 0029252734 scopus 로고
    • Multivariate SPC charts for monitoring batch processes
    • Nomikos P, MacGregor JF. Multivariate SPC charts for monitoring batch processes. Technometrics. 1995; 37: 41-59.
    • (1995) Technometrics , vol.37 , pp. 41-59
    • Nomikos, P.1    MacGregor, J.F.2
  • 28
    • 0030525683 scopus 로고    scopus 로고
    • Non-parametric confidence bounds for process performance monitoring charts
    • Martin EM, Morris AJ. Non-parametric confidence bounds for process performance monitoring charts. J Process Control. 1996; 6: 349-358.
    • (1996) J Process Control , vol.6 , pp. 349-358
    • Martin, E.M.1    Morris, A.J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.