A New Process Industry Fault Diagnosis Algorithm Based on Ensemble Improved Binary‐Tree SVM. Issue 2 (1st April 2015)

• Record Type: Journal Article
• Authors: Wang, Anna; Sha, Mo; Liu, Limei; Chu, Maoxiang
• Is Part Of: Chinese journal of electronics. Volume 24:Issue 2(2015)
• Page Start: 258
• Page End: 262
• Publication Date: 2015-04-01
• Subjects: blast furnaces -- chemical engineering computing -- chemical industry -- fault diagnosis -- learning (artificial intelligence) -- support vector machines -- trees (mathematics); process industry fault diagnosis algorithm -- ensemble improved binary‐tree SVM -- support vector machine -- small sample nonlinear high dimension classification problem -- improved pretreatment binary‐tree SVM -- weak learning machine -- blast furnace faults -- Tennessee Eastman process; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2015.04.006
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : Support vector machine (SVM) is an effective tool in deal with small sample, nonlinear and high dimension classification problems. In this paper, an improved pre‐treatment binary‐tree SVM is proposed to solve fault diagnosis. Furthermore an ensemble method is presented to establish ensemble SVM. Here the improved SVM isused as weak learning machine. The new ensemble SVM can improve the performance of single binary‐tree SVM. At the end, the new algorithm is applied to fault diagnosis of blast furnace faults and the Tennessee Eastman process (TEP). The experiments results show that the improved binary‐tree SVM algorithm has an excellent performance on diagnosis speed and accuracy.