东华大学学报（英文版）2006，Vol.23Issue(1)：53-58,6.
Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis
Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis
1
作者信息
- 1. Department of Automation, Shanghai Jiaotong University, Shanghai 200030;Department of Automation, Shanghai Jiaotong University, Shanghai 200030;Department of Automation, Shanghai Jiaotong University, Shanghai 200030
- 折叠
摘要
Abstract
On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process.Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study.Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.关键词
principal component analysis/multiple support vector machine/process monitoring/fault detection/fault diagnosisKey words
principal component analysis/multiple support vector machine/process monitoring/fault detection/fault diagnosis分类
化学化工引用本文复制引用
..Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis[J].东华大学学报（英文版）,2006,23(1):53-58,6.
