Fault detection based on block kernel principal component analysis and support vector machine

被引：0

作者：

Li J.-B. ^{[1
]}

Han B. ^{[2
]}

Feng S.-B. ^{[1
]}

Zhang J.-D. ^{[1
]}

Li Y. ^{[1
]}

Zhong K. ^{[1
]}

Han M. ^{[1
]}

机构：

[1] Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, Liaoning

[2] State Key Laboratory of Navigation and Safety Technology, Shanghai Ship and Shipping Research Institute, Shanghai

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2020年 / 37卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Block kernel principal component analysis; Fault detection; Feature extraction; Least square support vector machine;

D O I：

10.7641/CTA.2019.80923

中图分类号：

学科分类号：

摘要：

The measurement data of industrial system is nonlinear and difficult to extract the characteristic information. In the complex large-scale industrial process, an integrated fault detection method based on the block kernel principal component analysis (BKPCA) and least squares support vector machine (LS-SVM) is proposed. Firstly, the measurement variables are partitioned. And KPCA is used to establish the T2 and squared prediction error (SPE) monitoring statistics in the feature space for each block to monitor the health status in real time. The LS-SVM is used to rejudge the faulty data detected by above process. Calculating the contribution rate of each block after the fault occurs, and then the faulty block can be determined. Due to the parallel block algorithm, the location of the fault can be simply found, and the computational efficiency is improved. What is more, the application of LS-SVM can also improve the accuracy of fault detection. The Tennessee-Eastman (TE) process data is used to verify the method proposed in this paper. The results show the effectiveness of proposed method. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.

引用

页码：847 / 854

页数：7