WANG Anna, SHA Mo, LIU Limei, CHU Maoxiang. A New Process Industry Fault Diagnosis Algorithm Based on Ensemble Improved Binary-Tree SVM[J]. Chinese Journal of Electronics, 2015, 24(2): 258-262. doi: 10.1049/cje.2015.04.006
Citation: WANG Anna, SHA Mo, LIU Limei, CHU Maoxiang. A New Process Industry Fault Diagnosis Algorithm Based on Ensemble Improved Binary-Tree SVM[J]. Chinese Journal of Electronics, 2015, 24(2): 258-262. doi: 10.1049/cje.2015.04.006

A New Process Industry Fault Diagnosis Algorithm Based on Ensemble Improved Binary-Tree SVM

doi: 10.1049/cje.2015.04.006
Funds:  This work is supported by the National Natural Science Foundation of China (No.61050006).
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  • Corresponding author: SHA Mo was born in 1979. He received the M.S. degree from Northeastern University of China in 2006. His current research interests include machine learning and fault diagnosis. (Email:desert1979@163.com)
  • Publish Date: 2015-04-10
  • 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 is used 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.
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