(Key Laboratory of Condition Monitoring and Control for Power Plant Equipment, Ministry of Education, North China Electric Power University Beijing, 102206, China) 在期刊界中查找 在百度中查找 在本站中查找
In order to improve the classification ability and diagnostic accuracy of turbine vibration signals, a new feature extraction method from fault signals of turbine vibration based on the manifold learning method (MLM) is proposed. The results show that the MLM effectively extracts fault feature information of turbine vibration and separates different types of fault feature information. The diagnostic accuracy of features extracted by the MLM is significantly higher than that of the wavelet packet analysis method.