Multimode Process Monitoring Based on the Density-Based Support Vector Data Description  

作　　者：郭红杰 王帆 宋冰 侍洪波 谭帅 

机构地区：[1]Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University ofScience and Technology, Shanghai 200237, China

出　　处：《Journal of Donghua University(English Edition)》2017年第3期342-348,共7页东华大学学报（英文版）

基　　金：National Natural Science Foundation of China(No.61374140);the Youth Foundation of National Natural Science Foundation of China(No.61403072)

摘　　要：Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the samples which are sparse in the mode.To solve this issue,a new approach called density-based support vector data description( DBSVDD) is proposed. In this article,an algorithm using Gaussian mixture model( GMM) with the DBSVDD technique is proposed for process monitoring. The GMM method is used to obtain the center of each mode and determine the number of the modes. Considering the complexity of the data distribution and discrete samples in monitoring process,the DBSVDD is utilized for process monitoring. Finally,the validity and effectiveness of the DBSVDD method are illustrated through the Tennessee Eastman( TE) process.Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the samples which are sparse in the mode.To solve this issue,a new approach called density-based support vector data description（ DBSVDD） is proposed. In this article,an algorithm using Gaussian mixture model（ GMM） with the DBSVDD technique is proposed for process monitoring. The GMM method is used to obtain the center of each mode and determine the number of the modes. Considering the complexity of the data distribution and discrete samples in monitoring process,the DBSVDD is utilized for process monitoring. Finally,the validity and effectiveness of the DBSVDD method are illustrated through the Tennessee Eastman（ TE） process.

关 键 词：Eastman Tennessee sparse utilized illustrated kernel Bayesian charts validity false 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]