Mean Shifts Identification Model in Bivariate Process Based on LS-SVM Pattern Recognizer

This study develops a least squares support vector machines (LS-SVM) based model for bivariate process to diagnose abnormal patterns of process mean vector, and to help identify abnormal variable(s) when Shewhart-type multivariate control charts based on Hotelling's T 2 are used. On the basis of studying and defining the normal/abnormal patterns of the bivariate process mean shifts, a LS-SVM pattern recognizer is constructed in this model to identify the abnormal variable(s). The model in this study can be a strong supplement of the Shewhart-type multivariate control charts. Furthermore, the LS-SVM techniques introduced in this research can meet the requirements of process abnormalities diagnosis and causes identification under the condition of small sample size. An industrial case application of the proposed model is provided. The performance of the proposed model was evaluated by computing its classification accuracy of the LS-SVM pattern recognizer. Results from simulation cas...

• Type: Journal
• Year: 2010
• Where: JDCTA
• Authors: Zhi-Qiang Cheng, Yi-Zhong Ma, Jing Bu
• Keywords: Abnormal, Bivariate Process, JDCTA 2010, Model