scholarly journals The Application of Dual-Tree Complex Wavelet Transform (DTCWT) Energy Entropy in Misalignment Fault Diagnosis of Doubly-Fed Wind Turbine (DFWT)

Entropy ◽  
2017 ◽  
Vol 19 (11) ◽  
pp. 587 ◽  
Author(s):  
Yancai Xiao ◽  
Yi Hong ◽  
Xiuhai Chen ◽  
Weijia Chen
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Complex Wavelet Transform ◽  
Energy Entropy ◽  
Complex Wavelet ◽  
Doubly Fed ◽  
Misalignment Fault

scholarly journals Pattern Recognition of Partial Discharge by Using Scale parameters-Energy Entropy Characteristic Pairs

2019 ◽  
Vol 136 ◽  
pp. 01026
Author(s):  
Liu Dongchao ◽  
Xiong Hui ◽  
Zhu Xiaotong ◽  
Xu Lei
Keyword(s):  
Wavelet Transform ◽  
Partial Discharge ◽  
Structure Characteristic ◽  
Complex Wavelet Transform ◽  
Scale Parameters ◽  
Energy Entropy ◽  
Feature Information ◽  
Complex Wavelet ◽  
Feature Dimension ◽  
Entropy Characteristic

In this paper, the complex wavelet transform (CWT) was used to process the ultra-high frequency partial discharge (UHF PD) signal in gas insulated switchgear (GIS) at different scales. The trend curves of complex wavelet transform energy entropy (CWT-EE) under different decomposition scale were analyzed, and it was found that the PD feature information mainly distributed in the scales, in which the gradient of CWT-EE is big. Besides, The CWT-EE characteristics and their scales were extracted to the structure characteristic pairs for PD type identification. The recognition results show that the characteristic pair could effectively identify four typical defects in GIS and obviously reduce the feature dimension.

Bearing Fault Diagnosis Based on Dual-Tree Complex Wavelet Transform

2012 ◽  
Vol 490-495 ◽  
pp. 128-132
Author(s):  
Hui Li
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Complex Wavelet Transform ◽  
Noise Condition ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Complex Wavelet ◽  
Envelope Spectrum

A novel method of bearing fault diagnosis based on demodulation technique of dual-tree complex wavelet transform (DTCWT) is proposed. It is demonstrated that the proposed dual-tree complex wavelet transform has better shift invariance, reduced frequency aliasing effect and de-noising ability. The bearing fault vibration signal is firstly decomposed and reconstructed using dual-tree complex wavelet transform. Then the real and imaginary parts are obtained and the vibration signal is amplitude demodulated. In the end, the amplitude envelope and wavelet envelope spectrum are computed. Therefore, the character of the bearing fault can be recognized according to the wavelet envelope spectrum. The experimental results show that dual-tree complex wavelet transform can effectively reduce spectral aliasing and fault diagnosis based on dual-tree complex wavelet transform can effectively diagnose bearing inner and outer race fault under strong background noise condition.

Fault Diagnosis of Rolling Bearing Based on Dual-Tree Complex Wavelet Transform and AR Power Spectrum

2013 ◽  
Vol 819 ◽  
pp. 271-276 ◽  
Author(s):  
Zhi Peng Meng ◽  
Yong Gang Xu ◽  
Guo Liang Zhao ◽  
Sheng Fu
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Power Spectrum ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Complex Wavelet Transform ◽  
Noise Interference ◽  
Diagnosis Method ◽  
Complex Wavelet ◽  
Effective Signal

Aiming at the strong background noise involved in the signals of rolling bearing and the difficulty to extract fault feature in practice, a new fault diagnosis method is proposed based on Dual-tree Complex Wavelet Transform (DT-CWT) and AR power spectrum. Firstly, the non-stationary and complex vibration signal is decomposed into several different frequency band components through dual-tree complex wavelet decomposition; Secondly, Hilbert envelope is formed from the components which contains the fault information. Finally, the auto-power spectrum can be obtained by auto-regressive (AR) spectrum. The noise interference was eliminated effectively, and the effective signal information was retained at the same time. Thus, the fault feature information was extracted. In this paper, the fault test and the engineering practical fault data of rolling bearing were analyzed by dual-tree complex wavelet transform and AR power spectrum. The results show that the noise of the vibration signal was eliminated effectively, and the fault feature were extracted. The feasibility and effectiveness of the method were verified.

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