Multi-class fault diagnosis of BF based on global optimization LS-SVM

ZHANG Hai-gang; ZHANG Sen; YIN Yi-xin

doi:10.13374/j.issn2095-9389.2017.01.005

Volume 39 Issue 1

Jan. 2017

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ZHANG Hai-gang, ZHANG Sen, YIN Yi-xin. Multi-class fault diagnosis of BF based on global optimization LS-SVM[J]. Chinese Journal of Engineering, 2017, 39(1): 39-47. doi: 10.13374/j.issn2095-9389.2017.01.005

Citation:

ZHANG Hai-gang, ZHANG Sen, YIN Yi-xin. Multi-class fault diagnosis of BF based on global optimization LS-SVM[J]. Chinese Journal of Engineering, 2017, 39(1): 39-47. doi: 10.13374/j.issn2095-9389.2017.01.005

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Multi-class fault diagnosis of BF based on global optimization LS-SVM

doi: 10.13374/j.issn2095-9389.2017.01.005

ZHANG Hai-gang^1,2,
ZHANG Sen^{1,2
,
,},
YIN Yi-xin^1,2

1) School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2) Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education), University of Science and Technology Beijing, Beijing 100083, China

Received Date: 2016-03-16

Abstract

Abstract

Aiming at the requirement of high speed and precision in blast furnace fault diagnosis systems, a new strategy based on global optimization least-squares support vector machines (LS-SVM) was proposed to solve this problem. Firstly, the variable metric discrete particle swarm optimization algorithm was employed to optimize the feature selection and LS-SVM parameters. Secondly, the feature vector was compressed by kernel principal component analysis. Finally, the heuristic error correcting output codes were constructed on the basis of Fisher linear discriminate rate. In the fault diagnosis scheme, fewer LS-SVM classifiers were applied through meaningful partitions and recombination of fault training samples. Simulation results show that the proposed fault diagnosis method can not only improve the fault detection accurate rate, but also enhance the timeliness of the entire system.
- blast furnaces,
- fault diagnosis,
- least-squares analysis,
- support vector machines,
- global optimization

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References(7)

References

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