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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.

has been cited by the following article:

• TITLE: An Integrated Use of Advanced T2 Statistics and Neural Network and Genetic Algorithm in Monitoring Process Disturbance

  AUTHORS: Xiuhong WANG

  KEYWORDS: T2 Statistics, Neural Networks, Statistical Process Control, Engineering Process Control, Genetic Algorithm

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.2 No.5, December 28, 2009

  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.