A New Method to Mechanical Fault Classification with Support Vector Machine

In this paper, the basic principle of support vector machine is introduced firstly, Then a new method to diagnosis fault for high voltage circuit breakers is presented based on the introduction of wavelet packet and characteristic entropy. The new method decomposes vibration signals with wavelet pac...

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Bibliographic Details
Published in2010 International Conference on Intelligent System Design and Engineering Application Vol. 2; pp. 833 - 837
Main Authors Laijun Sun, Mingliang Liu, Haibo Qian, Guangzhong Ye
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2010
Subjects
Artificial neural networks
Circuit breakers
Circuit faults
Entropy
fault diagnosis
HV circuit breaker
neural network
Support Vector Machine
Support vector machines
Training
Vibrations
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Summary:In this paper, the basic principle of support vector machine is introduced firstly, Then a new method to diagnosis fault for high voltage circuit breakers is presented based on the introduction of wavelet packet and characteristic entropy. The new method decomposes vibration signals with wavelet packet, and extracts entropy parameters from the restructured signals at the third level. Finally, the new method and SVM are applied to the fault recognition of circuit breakers, and the usable process is introduced in detail in the paper. In addition, SVM is compared with the artificial neural network, and the paper concludes that in terms of classification and learning speed, SVM is better than neural network clearly, and SVM is more applicable to fault recognition of circuit breakers.
ISBN:1424483336
9781424483334
DOI:10.1109/ISDEA.2010.88