Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet energy spectrum

2017 ◽  
Vol 12 (3) ◽  
pp. 406-419 ◽  
Author(s):  
Yun Kong ◽  
Tianyang Wang ◽  
Zheng Li ◽  
Fulei Chu
Keyword(s):  
Feature Extraction ◽  
Energy Spectrum ◽  
Wind Turbine ◽  
Wind Turbine Gearbox ◽  
Spectral Kurtosis ◽  
Wavelet Energy ◽  
Planet Gear ◽  
Fault Feature Extraction

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.

Feature Extraction Method for Rolling Bearing’s Week Fault Based on Kalman Filter and FSK

2014 ◽  
Vol 574 ◽  
pp. 684-689
Author(s):  
Zhi Chuan Liu ◽  
Li Wei Tang ◽  
Li Jun Cao
Keyword(s):  
Feature Extraction ◽  
Kalman Filter ◽  
Extraction Method ◽  
Model Parameters ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Feature Extraction Method ◽  
Spectral Kurtosis ◽  
Band Pass ◽  
Fault Feature Extraction

Aiming at the problem that traditional demodulated resonance technology has the deficiency of difficulty to choose the parameters of band-pass filter, Kalman filter technology and fast spectral kurtosis were combined for fault feature extraction of rolling bearing. AR model was firstly built with gearbox original vibration signals, and then model order was ascertained with AIC formula, and finally model parameters were calculated with least-squares method. The original signals were pretreated by Kalman filter. Fast spectral kurtosis (FSK) was used to choose parameters of the best band-pass filter, and finally fault diagnosis was achieved by the energy operator demodulation spectrum analysis of band-pass filtered signal. The analysis result of engineering signals indicated that fault feature extraction method based on Kalman filter and fast spectral kurtosis can primely provide a new feature extraction method for rolling bearing’s week fault.

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