원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.6 No.6
2013.11
pp.101-108
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
In allusion to the lack of a large number of fault data samples in machinery fault diagnosis, the thesis put forward a new method of machinery fault diagnosis based on support vector machine. And the principle and algorithm of the method has been introduced. Then the multi-fault classifier has been built using simulative fault data. This diagnostic method only need a small amount of time-domain fault data samples to train the fault classification, and don’t need to extract the feature amount. In addition, we verify the correctness of this fault classifier through the application of fault classification in steam turbine generator set. The diagnostic method is simple and has strong ability of fault classification.
목차
Abstract
1. Introduction
2. Basic principle of support vector machine classification
3. The Choice of Kernel Function
4. Fault Diagnosis of Steam Turbine Generator Set
4.1. The Establishment of Multi-Fault Classifier
4.2. The Testing Result
5. Conclusions
Acknowledgements
References
1. Introduction
2. Basic principle of support vector machine classification
3. The Choice of Kernel Function
4. Fault Diagnosis of Steam Turbine Generator Set
4.1. The Establishment of Multi-Fault Classifier
4.2. The Testing Result
5. Conclusions
Acknowledgements
References
키워드
저자정보
참고문헌
자료제공 : 네이버학술정보