文献类型：期刊文献

英文题名：Fault Classification in Dynamic Processes Using Multiclass Relevance Vector Machine and Slow Feature Analysis

作者：Huang, Jian[1,2];Yang, Xu[1];Shardt, Yuri A. W.[3];Yan, Xuefeng[2]

机构：[1]Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Minist Educ, Key Lab Knowledge Automat Ind Proc, Beijing 100083, Peoples R China;[2]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[3]Tech Univ Ilmenau, Dept Automat Engn, D-98684 Ilmenau, Germany

年份：2020

卷号：8

起止页码：9115

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20200107979384);WOS:【SCI-EXPANDED(收录号:WOS:000525423400018)】；

基金：This research was supported in part by the National Natural Science Foundation of China under Grant 61903026 and Grant 61673053, in part by the China Postdoctoral Science Foundation under Grant 2019M660462, in part by the Fundamental Research Funds for the Central Universities under Grant 222201917006, and in part by the National Key Research and Development Program of China under Grant 2017YFB0306403.

语种：英文

外文关键词：Slow feature analysis; relevance vector machine; dynamic process; fault classification; process monitoring; statistical learning; support vector machine; feature extraction; process control

摘要：This paper proposes a modified relevance vector machine with slow feature analysis fault classification for industrial processes. Traditional support vector machine classification does not work well when there are insufficient training samples. A relevance vector machine, which is a Bayesian learning-based probabilistic sparse model, is developed to determine the probabilistic prediction and sparse solutions for the fault category. This approach has the benefits of good generalization ability and robustness to small training samples. To maximize the dynamic separability between classes and reduce the computational complexity, slow feature analysis is used to extract the inner dynamic features and reduce the dimension. Experiments comparing the proposed method, relevance vector machine and support vector machine classification are performed using the Tennessee Eastman process. For all faults, relevance vector machine has a classification rate of 39;, while the proposed algorithm has an overall classification rate of 76.1;. This shows the efficiency and advantages of the proposed method.