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

WANG Anna; SHA Mo; LIU Limei; CHU Maoxiang

doi:10.1049/cje.2015.04.006

Volume 24 Issue 2

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Article Navigation > Chinese Journal of Electronics > 2015 > 24(2): 258-262

WANG Anna, SHA Mo, LIU Limei, et al., “A New Process Industry Fault Diagnosis Algorithm Based on Ensemble Improved Binary-Tree SVM,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 258-262, 2015, doi: 10.1049/cje.2015.04.006

Citation:

WANG Anna, SHA Mo, LIU Limei, et al., “A New Process Industry Fault Diagnosis Algorithm Based on Ensemble Improved Binary-Tree SVM,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 258-262, 2015, doi: 10.1049/cje.2015.04.006

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

doi: 10.1049/cje.2015.04.006

College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61050006).

More Information

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

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 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.
- Multi-classification,
- Binary-tree SVM,
- Ensemble-learning,
- Fault diagnosis

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References(12)

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