Source(s) Identification of Variance Shifts in Bivariate Process Using LS-SVM Based Pattern Recognition Model

摘要：

MSPC techniques are effective tools for detecting the abnormalities of process variation. But MSPC charts do not provide the necessary information about which process variables (or subset of them) are responsible for the signal. In order to identify the process abnormality in covariance matrix of bivariate process, this article proposes a model based on LSSVM pattern recognizer and |S| chart method, the main property of this model is to identify the assignable causes through LS-SVM pattern recognizer technique when |S| chart issue a warning signal. The simulation results indicate that the proposed model is feasible and effective. A bivariate example is presented to illustrate the application of the proposed model.

关键词： multivariate statistical control quality diagnosis Least squares support vector machine pattern recognition

作者: Cheng Zhiqiang Ma Yizhong

作者单位: Department of Management Science and Engineering,Nanjing University of Science and Technology,Nanjing Jiangsu,210094,P.R.China

会议类型: 国际会议

会议名称: 2010 International Conference on Material and Manufacturing Technology(2010材料与制造技术国际会议 ICMMT2010)

会议地点: 重庆

会议语种:英文

页码: 891-896

在线出版日期: 2010-09-17（万方平台首次上网日期，不代表论文的发表时间）