An Integrated Use of Advanced T2 Statistics and Neural Network and Genetic Algorithm in Monitoring Process Disturbance
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Abstract
Integrated use of statistical process control (SPC) and engineering process control (EPC) has better performance than that by solely using SPC or EPC. But integrated scheme has resulted in the problem of “Window of Opportunity” and autocorrelation. In this paper, advanced T2 statistics model and neural networks scheme are combined to solve the above problems: use T2 statistics technique to solve the problem of autocorrelation; adopt neural networks technique to solve the problem of “Window of Opportunity” and identification of disturbance causes. At the same time, regarding the shortcoming of neural network technique that its algorithm has a low speed of convergence and it is usually plunged into local optimum easily. Genetic algorithm was proposed to train samples in this paper. Results of the simulation ex-periments show that this method can detect the process disturbance quickly and accurately as well as identify the dis-turbance type.
- F. Tsung and D. W. Apley, “The dynamic T2 chart for monitoring feedback-controlled processes,” IIE Transac-tion, Vol. 34, pp. 1043–1053, 2002.
- X. H. Wang, G. P. Han, and Q. Q. Li, “A joint-monitor-ing method of process disturbance under an integrated SPC/EPC model,” Proceedings of the 38th international conference on computers and industrial engineering, Zhengzhou, 2008.
- C. H. Huang and Y. N. Lin, “Decision rule of assignable causes removal under an SPC/EPC integration system,” International journal of systems science, Vol. 33, No. 10, pp. 855–867, 2002.
- D. W. Apley and F. Tsung, “The autoregressive T2 chart for monitoring univariate autocorrelated processes,” Journal of Quality Technology, Vol. 34, No. 1, pp. 79–95, 2002.
- F. Tsung, “Run to run proportional integral derivative process control and monitoring schemes,” A dissertation submitted in partial fulfillment of the requirements for degree of Doctor of Philosophy, University of Michigan, pp. 121–123, 1997.
- G. Box and T. Kramer, “Statistical process monitoring and feedback adjustment-a discussion,” American statis-tical Association and the American Society for Quality Control, Journal Technometrics, Vol. 34, No. 3, pp. 251–267, 1992.
- F. B. Alt, “Multivariate quality control,” in Encyclopedia of the statistical Science, eds. N. L. Johnson, S. Kotz and C. R. Read, New York, John Wiley, pp. 111–122.
- D. C. Montgomery, J. B. Keats, G. Runger and W. Messina, “Integrating statistical process control and en-gineering process control,” Journal Quality Technology, Vol. 26, No. 2, pp. 79–87, 1994.
- H. Hotelling, “Multivariate quality control-illustrated by air testing of sample bombsights,” Techniques of Statis-tical Analysis, Mcgraw-Hill, New York, pp. 11–184, 1947.
- B. Cheng and D. M. Titterington, “Neural networks: A review from a statistical perspective,” Statistical Science, Vol. 9, pp. 2–54, 1994.
- G. D. Feng and C. Chen, “Nonlinear inversion of poten-tial-field data using an improved genetic algorithm,” Journal of China University of Geosciences, Vol. 15, No. 4, pp. 25–31, 2004.
- C. C. CHIU, “Identification of process disturbance using SPC/EPC and neural networks,” Journal of intelligent manufacturing, Vol. 14, pp. 379–388, 2003.
- J. Neter, W. Wasserman, and G. A. Whitmore, Applied Statistics, Boston, Allyn and Bacon, 1979.