Fault diagnosis for train plug door using weighted fractional wavelet packet decomposition energy entropy

2022 ◽  
Vol 166 ◽  
pp. 106549
Author(s):  
Yongkui Sun ◽  
Yuan Cao ◽  
Peng Li
2016 ◽  
Vol 31 (1) ◽  
pp. 303-313 ◽  
Author(s):  
Chun Gan ◽  
Jianhua Wu ◽  
Shiyou Yang ◽  
Yihua Hu ◽  
Wenping Cao

2015 ◽  
Vol 727-728 ◽  
pp. 872-875
Author(s):  
Wen Bo Na ◽  
Qing Feng Jiang ◽  
Zhi Wei Su

In order to improve the accuracy of diagnosis pumping, and accelerate the speed of diagnosis, a fault diagnosis model based on improved extreme learning machine (RWELM) was proposed. Firstly, it extracted the energy characteristic eigenvector of dynamometer cards of an oilfield in northern Shanxi by using wavelet packet decomposition method. Then through simulation of fault diagnosis, and compare with the extreme learning machine (ELM), RBF neural networks and support vector machine (SVM). The experimental results show that the accuracy and the speed of fault diagnosis based on the RWELM are better than the ELM, RBF neural network and SVM.


2010 ◽  
Vol 121-122 ◽  
pp. 813-818 ◽  
Author(s):  
Wei Guo Zhao ◽  
Li Ying Wang

On the basis of wavelet packet-characteristic entropy(WP-CE) and multiclass fuzzy support vector machine(MFSVM), the author proposes a new fault diagnosis method of vibrating of hearings,in which three layers wavelet packet decomposition of the acquired vibrating signals of hearings is performed and the wavelet packet-characteristic entropy is extracted,the eigenvector of wavelet packet of the vibrating signals is constructed,and taking this eigenvector as fault sample multiclass fuzzy support vector machine is trained to implement the intelligent fault diagnosis. The simulation result from the proposed method is effective and feasible.


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