The fault diagnosis of rolling bearing in gearbox of wind turbines based on second generation wavelet

A new rolling bearing fault diagnosis approach is proposed. The original vibration signal is purified using the second generation wavelet denoising. The purified signal is further decomposed by an improved ensemble empirical mode decomposition (EEMD) method. A new selection criterion, including correlation analysis and the first two intrinsic mode functions (IMFs) with the maximum energy, is put forward to eliminate the pseudo low-frequency components. Experimental investigation show that the rolling bearing fault features can be effectively extracted.

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Turbine machine fault diagnosis using modified redundant second generation wavelet packet transform

Proceedings of the 10th World Congress on Intelligent Control and Automation ◽

10.1109/wcica.2012.6358409 ◽

2012 ◽

Cited By ~ 2

Author(s):

Ning Li ◽

Rui Zhou

Keyword(s):

Fault Diagnosis ◽

Second Generation ◽

Wavelet Packet ◽

Wavelet Packet Transform ◽

Machine Fault Diagnosis ◽

Machine Fault ◽

Second Generation Wavelet

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Rolling Bearing Fault Diagnosis Based on Wavelet Packet and Improved BP Neural Network for Wind Turbines

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.347-350.117 ◽

2013 ◽

Vol 347-350 ◽

pp. 117-120

Author(s):

Zhao Ran Hou

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Frequency Domain ◽

Wind Turbines ◽

Wavelet Packet ◽

Roller Bearing ◽

Rolling Bearing ◽

Transmission System ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Vibration signal was a carrier of fault features of the wind turbine transmission system, it can reflect most of the fault information of the wind turbine transmission system. According to the frequency domain features of the roller bearing fault, wavelet packet transform for feature extraction was proposed as the characteristics of wind turbines in the presence of a large number of transient and non-stationary signals. The characteristics of wavelet packet was analyzed, combined with the wind turbines in the rolling bearing fault characteristic vibration extraction methods, the rolling bearing fault diagnosis was realized through the wavelet packet decomposition and reconstruction, the procedure was given. The simulation result shows that this application can reflect relationship of the failure characteristics and frequency domain feature vectors, also the nonlinear mapping ability of neural networks was played and the fault diagnosis capability enhanced.

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Signal Denoising Method Based on Adaptive Redundant Second-Generation Wavelet for Rotating Machinery Fault Diagnosis

Mathematical Problems in Engineering ◽

10.1155/2016/2727684 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Na Lu ◽

Guangtao Zhang ◽

Yuanchu Cheng ◽

Diyi Chen

Keyword(s):

Fault Diagnosis ◽

Second Generation ◽

Vibration Signal ◽

Rotating Machinery ◽

Support Vector ◽

Signal Denoising ◽

Denoising Method ◽

Machine Method ◽

Second Generation Wavelet ◽

Machinery Fault Diagnosis

Vibration signal of rotating machinery is often submerged in a large amount of noise, leading to the decrease of fault diagnosis accuracy. In order to improve the denoising effect of the vibration signal, an adaptive redundant second-generation wavelet (ARSGW) denoising method is proposed. In this method, a new index for denoising result evaluation (IDRE) is constructed first. Then, the maximum value of IDRE and the genetic algorithm are taken as the optimization objective and the optimization algorithm, respectively, to search for the optimal parameters of the ARSGW. The obtained optimal redundant second-generation wavelet (RSGW) is used for vibration signal denoising. After that, features are extracted from the denoised signal and then input into the support vector machine method for fault recognition. The application result indicates that the proposed ARSGW denoising method can effectively improve the accuracy of rotating machinery fault diagnosis.

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