Pressure vessel state investigation based upon the least squares support vector machine
In view of the remarkable time-frequency property obtained from wavelet packets and the excellent generalization ability derived from the least squares support vector machine (LS SVM), a novel approach is proposed, which focuses on the research on state detection for pressure vessels. The minimum entropy criterion is adopted to realize the optimal wavelet packet decomposition, the feature vectors being established according to the percentage of singleband signal energy in the total energy. In addition, the LS SVM is introduced to accomplish classification, for judging the states of pressure vessels. The test results show that high classification accuracy is achieved compared with the cases for the original SVM and BP neural networks under the same conditions. The scheme proposed is proved to be an accurate one for identifying the various states, which can be adapted to wide practical applications.
Pressure vessel Wavelet packet analysis Feature extraction Least squares support vector machine
Jichen Shen Hongfei Chang Yang Li
School of Automation Engineering, Northeast Dianli University, Jilin 132012, PR China
国际会议
南昌
英文
883-887
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）