Open AccessA New Process Industry Fault Diagnosis Algorithm Based on Ensemble Improved Binary‐Tree SVM
Author(s) -
Wang Anna,
Sha Mo,
Liu Limei,
Chu Maoxiang
Publication year - 2015
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2015.04.006
Subject(s) - support vector machine , binary tree , computer science , binary number , artificial intelligence , pattern recognition (psychology) , fault (geology) , process (computing) , algorithm , nonlinear system , structured support vector machine , tree (set theory) , ensemble learning , ranking svm , machine learning , data mining , mathematics , mathematical analysis , physics , arithmetic , quantum mechanics , seismology , geology , operating system
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.