Effective pattern recognition of control charts using a dynamically trained learning vector quantization network

Journal of the Chinese Institute of Industrial Engineers

Volumn 25, Issue 1, 2008, Pages 73-89

  Guh, Ruey Shiang ^a   Shiue, Yeou Ren ^b  

^a NATIONAL FORMOSA UNIVERSITY   (Taiwan)

^b HUAFAN UNIVERSITY   (Taiwan)

Author keywords

Control chart; Learning vector quantization; Pattern recognition; Static artificial neural network; Statistical process control

Indexed keywords

BACKPROPAGATION; CONTROL CHARTS; DEEP NEURAL NETWORKS; FLOWCHARTING; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; PATTERN RECOGNITION; PROCESS CONTROL; REAL TIME SYSTEMS; STATISTICAL PROCESS CONTROL;

BACKPROPAGATION NETWORK; CONTROL CHART PATTERNS (CCPS); LEARNING VECTOR QUANTIZATION; LEARNING VECTOR QUANTIZATION NETWORKS; MACHINE LEARNING TECHNIQUES; PROCESS VARIATION; RECOGNITION ACCURACY; TRAINING ALGORITHMS;

VECTOR QUANTIZATION;

EID: 50949122016     PISSN: 10170669     EISSN: 21517606     Source Type: Journal    
DOI: 10.1080/10170660809509074     Document Type: Article

References (41)

1. Cheng, C. S., and Tzeng, C. A., 1994. A neural network approach for detecting shifts in the process mean and variability. Journal of the Chinese Institute of Industrial Engineers, 11:67–75.
2. Cheng, C. S., 1997. A neural network approach for the analysis of control chart patterns. International Journal of Production Research, 35:667–697.
3. Chiu, C. C., and Yang, M. H., 2001. Shifts recognition in correlated manufacturing process parameters using neural network. Journal of Chinese Institute of Industrial Engineers, 18:86–94.
4. Davis, R. B., and Woodall, W. H., 1988. Performance of the control chart trend rule under linear shift. Journal of Quality Technology, 20:260–262.
5. Desieno, D., 1988. “ Adding a conscience to competitive learning ”. In International Joint Conference on Neural Networks Vol. 1, 117–124. San Diego, CA:IEEE.
6. Dieterle, F., Muller-Hagedorn, S., Liebich, H. M., and Gauglitz, G., 2003. Urinary nucleosides as potential tumor markers evaluated by learning vector quantization. Artificial Intelligence in Medicine, 28:265–279.
7. Duncan, A. J., 1986. Quality Control and Industrial Statistics, 5th ed. Illinois:Irwin Book Company.
8. Fukushima, K., and Miyake, S., 1983. Neocognitron:a neural network model for a mechanism of visual pattern recognition. IEEE Transaction on Systems, Man, and Cybernetics, 13:826–834.
9. Gan, F. F., 1992. CUSUM control charts under linear drift. The Statistician, 41:71–84.
10. Gardner, J. W., Shin, H. W., Hines, E. L., and Dow, C. S., 2000. An electronic nose system for monitoring the quality of potable water. Sensors and Actuators B, 69:336–341.
11. Grant, L. E., and Leavenworth, R. S., 1996. Statistical Quality Control, 7th ed. New York:McGraw-Hill Book Company.
12. Guh, R. S., and Tannock, J. D. T., 1999. Recognition of control chart concurrent patterns using a neural network approach. International Journal of Production Research, 37:1743–1765.
13. Guh, R. S., Zorriassatine, F., Tannock, J. D. T., and O'Brien, C., 1999. On-line control chart pattern detection and discrimination—A neural network approach. Artificial Intelligence in Engineering, 13:413–425.
14. Guh, R. S., 2002. Effects of non-normality on neural network based control chart pattern recognition. Journal of the Chinese Institute of Industrial Engineers, 19:13–22.
15. Guh, R. S., 2005. A hybrid learning-based model for on-line detection and analysis of control chart patterns. Computers & Industrial Engineering, 49:35–62.
16. Hassan, A., Shariff, Nabi Baksh M., Shaharoun, A. M., and Jamaluddin, H., 2003. Improved SPC chart pattern recognition using statistical features. International Journal of Production Research, 41:1587–1603.
17. Hwarng, H. B., 2005. Simultaneous identification of mean shift and correlation change in AR(1) processes. International Journal of Production Research, 43:1761–11783.
18. Kohonen, T., 1995. Self Organization Maps Heidelberg:Springer-Verlag.
19. Kohonen, T., 1988. An introduction to neural computing. Neural Networks, 1:3–16.
20. Kohonen, T., Barna, G., and Chrisley, R., 1988. “ Statistical pattern recognition with neural networks:benchmarking studies ”. In International Joint Conference on Neural Networks Vol. 1, 61–68. San Diego, CA:IEEE.
21. Law, A. M., and Kelton, W. D., 1982. Simulation Modelling and Analysis New York, NY:McGraw-Hill.
22. Lucas, J. M., 1982. Combined Shewhart-CUSUM Quality Control Schemes. Journal of Quality Technology, 14:51–59.
23. Lucy-Bouler, T. L., 1993. “ Application to forecasting of neural network recognition of shifts and trends in quality control data ”. In WCNN'93—World Congress on Neural Networks Vol. 1, 631–633. Portland, UK
24. Lucy-Bouler, T. L., 1991. Using Autocorrelations, CUSUMs and Runs Rules for Control Chart Pattern Recognition:An Expert System Approach University of Alabama. Ph.D. dissertation
25. Medsker, L. R., 1995. Hybrid Intelligent Systems Boston, MA, USA:Kluwer Academic Publishers.
26. Nelson, L. S., 1984. The Shewhart control chart-tests for special causes. Journal of Quality Technology, 16:237–239.
27. Nelson, L. S., 1985. Interpreting Shewhart x-bar control charts. Journal of Quality Technology, 17:114–116.
28. Noorossana, R., Farrokhi, M., and Saghaei, A., 2003. Using neural networks to detect and classify out-of-control signals in autocorrelated processes. Quality and Reliability Engineering International, 19:493–504.
29. Pearlmutter, B. A., 1989. Learning state space trajectories in recurrent neural networks. Neural Computation, 1:263–269.
30. Pham, D. T., and Oztemel, E., 1994. Control chart pattern recognition using learning vector quantization networks. International Journal of Production Research, 32:721–729.
31. Rumelhart, D. E., Hinton, G. E., and Williams, R. J., 1986. “ Learning internal representations by error propagation,”. In Parallel Distributed Processing:Explorations in the Microstructure of Cognition Edited by:Rumelhart, D. E., and McClelland, J. L., 318–362. Cambridge, MA:MIT press.
32. Shaffer, R. E., Rose-Pehrsson, S. L., and McGill, R. A., 1999. A comparison study of chemical sensor array pattern recognition algorithms. Analytica Chimica Acta, 384:305–317.
33. Specht, D. F., 1990. Probabilistic neural networks. Neural Networks, 3:109–118.
34. Swift, J. A., and Mize, J. H., 1995. Out-of-control pattern recognition and analysis for quality control charts using Lisp-based systems. Computers and Industrial Engineering, 28:81–91.
35. Vuckovic, A., Radivojevic, V., Chen, A. C. N., and Popovic, D., 2002. Automatic recognition of alertness and drowsiness from EEG by an artificial neural network. Medical Engineering and Physics, 24:349–360.
36. Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., and Lang, K. J., 1989. Phoneme recognition using time-delay neural networks. IEEE Transactions on Acoustic, Speech and Signal Processing, 37:328–339.
37. Wang, T. Y., and Chen, L. H., 2002. Mean shifts detection and classification in multivariate process:a neural-fuzzy approach. Journal of Intelligent Manufacturing, 13:211–221.
38. Western Electric. 1958. Statistical Quality Control Handbook New York, NY:Western Electric Company.
39. Yang, M. S., and Yang, J. H., 2002. A fuzzy-soft learning vector quantization for control chart pattern recognition. International Journal of Production Research, 40:2721–2731.
40. Zhang, Z. P., Chen, H. G., Ye, S. W., and Zhao, J. W., 1996. Comparison of the BP training algorithm and LVQ neural networks for e, μ, π identification. Nuclear Instruments and Methods in Physics Research A, 379:271–275.
41. Zorriassatine, F., Guh, R. S., Parkin, R. M., and Coy, J., 2004. Integrating novelty detection, neural networks and conventional tools for pattern recognition in multivariate processes. Journal of Engineering Manufacture, Proceedings of the Institute of Mechanical Engineers, Part B, 218:779–793.