Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD

2011 ◽  
Vol 36 (8) ◽  
pp. 2146-2153 ◽  
Author(s):  
Yonghua Jiang ◽  
Baoping Tang ◽  
Yi Qin ◽  
Wenyi Liu
Keyword(s):  
Feature Extraction ◽  
Wind Turbine ◽  
Extraction Method ◽  
Morlet Wavelet ◽  
Feature Extraction Method

Research on Fault Feature Extraction Method of the Wind Turbine Gearbox Based on SVD and Improved HHT

2012 ◽  
Vol 220-223 ◽  
pp. 785-788
Author(s):  
Chang Zheng Chen ◽  
Quan Gu ◽  
Bo Zhou
Keyword(s):  
Feature Extraction ◽  
Singular Value Decomposition ◽  
Wind Turbine ◽  
Extraction Method ◽  
Random Noise ◽  
Singular Value ◽  
Wind Turbine Gearbox ◽  
Feature Extraction Method ◽  
Value Decomposition ◽  
Fault Feature Extraction

This paper researches fault feature extraction method based on singular value decomposition and the improved HHT method for non-stationary characteristics of wind turbine gearbox vibration signal. Firstly, through the signal phase space reconstruction, the singular value decomposition as a pre-filter, to preprocessing the signal, effectively weaken the random noise. Then using EEMD to improve the HHT method, decompose the denoising signal into a series of different time scales component of intrinsic mode functions. The fault characteristics of the signal are extracted by the Hilbert transform. Finally, simulating gearbox fault experiment to verify the effectively of the proposed method.

Research on Vibration Signal Feature Extraction Method to the Wind Turbine Generator

2014 ◽  
Vol 902 ◽  
pp. 370-377
Author(s):  
Guo Xin Wu ◽  
Yun Bo Zuo ◽  
Yan Hui Shi
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Power ◽  
Extraction Method ◽  
Vibration Signal ◽  
Power Equipment ◽  
Feature Extraction Method ◽  
Current State ◽  
Diagnosis Method

Aiming at the safe operation of the wind turbine, a feature extraction method of vibration signal based on the principle of blind source separation was proposed. Blind source and the current state of fault signal was separated and predicated by Using historical data of wind turbine vibration signal as the observation noise, and then fault diagnosis signal mechanical operation was analyzed, the HMM/SVM series fault diagnosis models structure was proposed. By calculating the matching degree of unknown signal and wind power equipment in the state using HMM, the features for SVM was formed to achieve the finally discriminant. The experimental results showed that the fault diagnosis method can precisely and effectively complete the wind power equipment, higher than pure HMM or SVM diagnostic accuracy.

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