On line detection of mean and variance shift using neural networks and support vector machine in multivariate processes

Applied Soft Computing Journal

Volumn 12, Issue 9, 2012, Pages 2973-2984

  Salehi, Mojtaba ^a   Kazemzadeh, Reza Baradaran ^a   Salmasnia, Ali ^a  

^a TARBIAT MODARES UNIVERSITY   (Iran)

Author keywords

Mean shift; Multivariate process; Neural network; Statistical process control; Support vector machine; Variance shift

Indexed keywords

CONTROL CHART PATTERN; CONTROL CHARTS; MACHINE LEARNING TECHNIQUES; MAGNITUDE OF SHIFTS; MEAN SHIFT; MULTIVARIATE PROCESS; MULTIVARIATE PROCESS CONTROL; MULTIVARIATE QUALITY CONTROL; ON-LINE ANALYSIS; ON-LINE DETECTION; OUT-OF-CONTROL; OUT-OF-CONTROL SIGNALS; RESEARCH FIELDS; RESEARCH RESULTS; VARIANCE SHIFTS;

FLOWCHARTING; NEURAL NETWORKS; SIGNAL DETECTION; STATISTICAL PROCESS CONTROL;

SUPPORT VECTOR MACHINES;

EID: 84863427646     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2012.04.024     Document Type: Article

References (28)

1. C.-S. Cheng, and H.-P. Cheng Using neural networks to detect the bivariate process variance shifts pattern Computers and Industrial Engineering 60 2 2011 269 278
2. F.B. Alt Multivariate quality control S. Kotz, N.L. Johnson, C.R. Read, The Encyclopedia of Statistical Sciences 1985 Wiley New York 110 122
3. F. Zorriassatine, and J.D.T. Tannock A review of neural networks for statistical process control Journal of Intelligent Manufacturing 9 1998 209 224
4. F. Zorriassatine, J.D.T. Tannock, and C.O. Brien Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes Computers and Industrial Engineering 44 2003 385 408
5. 2 chart signals Computers and Industrial Engineering 47 2004 195 205
6. S.T.A. Niaki, and B. Abbasi Fault diagnosis in multivariate control charts using artificial neural networks International Quality and Reliability Engineering 21 2005 825 840 (Pubitemid 41766868)
7. F. Aparisi, G. Avendano, and J. Sanz Techniques to interpret T2 control chart signals IIE Transactions 38 2006 647 657 (Pubitemid 43820943)
8. F. Aparisi, J. Sanz, and G. Avendano Neural networks to identify the out-of control variables when a MEWMA chart is employed Proceedings of the Applied Simulation and Modeling Palma De Mallorca, Spain 2007 29 31
9. R.S. Guh, and Y.R. Shiue An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts Computers and Industrial Engineering 55 2008 475 493
10. J.-b. Yu, and L.-f. Xi A neural network ensemble-based model for on-line monitoring and diagnosis of out of control signals in multivariate manufacturing processes Expert Systems with Applications 36 2009 909 921
11. T.T. El-Midany, M.A. El-Baz, and M.S. Abd-Elwahed A proposed framework for control chart pattern recognition in multivariate process using artificial neural networks Expert Systems with Applications 37 2010 1035 1042
12. C. Low, C.M. Hsu, and F.J. Yu Analysis of variations in a multi-variate process using neural networks International Journal of Advanced Manufacturing Technology 22 2003 911 921
13. C.-S. Cheng, and H.-P. Cheng Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines Expert Systems with Applications 35 2008 198 206
14. R.S. Guh A hybrid learning-based model for on-line detection and analysis of control chart patterns Computers and Industrial Engineering 49 2005 35 62
15. S. Lawrence, C.L. Ciles, and A.C. Tsoi Lessons in neural network training: overfitting may be harder than expected Proceedings of the Fourteenth National Conference on Artificial Intelligence, AAA1-97 1997 AAAl Press Menlo Park, CA 540 545
16. W.S. Sarle Stopped training and other remedies for overfitting Proceedings of the Twenty-seventh Symposium on the Interface of Computing Science and Statistics 1995 35 360
17. V. Vapnik The Nature of Statistical Learning Theory 1995 Springer New York
18. S. Abe Support Vector Machines for Pattern Classification 2010 Springer-Verlag New York Inc.
19. M. Salehi, A. Bahreininejad, and I. Nakhai On-line analysis of out of control signals in multivariate manufacturing processes using a hybrid learning-based model Neurocomputing 74 2011 2083 2095
20. R. Battiti Using mutual information for selecting features in supervised neural net learning IEEE Transactions on Neural Networks 5 1994 537 550
21. A.E. Smith X-bar and R control chart interpretation using neural computing International Journal of Production Research 32 1994 309 320
22. A. Hassan, M. Shariff Nabi Baksh, A.M. Shaharoun, and H. Jamaluddin Improved SPC chart pattern recognition using statistical features International Journal of Production Research 41 7 2003 1587 1603
23. R.S. Guh On-line identification and quantification of mean shifts in bivariate processes using a neural network-based approach Quality and Reliability Engineering International 23 3 2007 367 385
24. C.S. Cheng A multi-layer neural network model for detecting changes in the process mean Computers and Industrial Engineering 28 1995 51 61
25. J.J. Pignatiello, and G.C. Runger Comparisons of multivariate CUSUM charts Journal of Quality Technology 22 3 1990 173 186
26. C.A. Lowry, W.H. Woodall, C.W. Champ, and S.E. Rigdon Multivariate exponentially weighted moving average control chart Technometrics 34 1 1992 46 53
27. R.B. Crosier Multivariate generalizations of cumulative sum quality control schemes Technometrics 30 3 1988 29 1303
28. A. Grigoryan, and D. He Multivariate double sampling S charts for controlling process variability International Journal of Production Research 43 2005 715 730 (Pubitemid 41663567)