Fault Diagnosis Method of Wind Turbines Based on Wide Deep Convolutional Neural Network With Resampling and Principal Component Analysis

doi:10.12096/j.2096-4528.pgt.22178

Abstract

Abstract:

Fault diagnosis of wind turbines suffers from less training data and noises. A method based on wide deep convolutional neural network with resampling and principal component analysis was presented for the diagnosis of mechanical faults (that is the main fault component of wind turbines). The method adopted a variety of signal preprocessing methods such as resampling wavelet threshold denoising and principal component analysis to increase the information density and ensure the integrity of the information. After being trained with small amount of data, the network which has a powerful feature extraction capability could extract the fault signal in the time domain which will be further used for fault diagnosis. Experimental results were verified based on the real wind turbine data, demonstrating the effectiveness of this method.

Key words: wind turbine, wide deep convolutional neural network, resampling, wavelet threshold denoising, principal component analysis

CLC Number:

TK 83

Zhan LIU, Yanyang BAO, Dazi LI. Fault Diagnosis Method of Wind Turbines Based on Wide Deep Convolutional Neural Network With Resampling and Principal Component Analysis[J]. Power Generation Technology, 2023, 44(6): 824-832.

Figures/Tables 12

Fig. 1 Diagram of WDCNN structure

Fig. 2 Diagram of resampling vibration signal of high-speed shaft tooth breakage in a certain unit

Fig. 3 Comparison before and after wavelet threshold denoising

Fig. 4 Frequency domain diagram of high-speed shaft vibration signal of a certain unit before and after PCA dimensionality reduction

Fig. 5 Frequency domain data of high-speed shaft vibration signal for a certain unit

Fig. 6 Structure diagram based on WDCNN method

Fig. 7 Raw data of drive end of generator

Fig. 8 Raw data of non drive end of generator

Tab. 1 Fault diagnosis results of wind turbine generator at the same speed

类别	测试样本	分类准确率/%
发电机驱动端	50	100
发电机非驱动端	50	98

Tab. 2 Fault diagnosis results of fan generators at different rotational speeds

类别	测试样本	分类准确率/%
发电机驱动端	50	98
发电机非驱动端	50	96

Fig. 9 Raw data of drive end of gearbox

Tab. 3 Results of ablation experiment

风机编号	训练集				测试集
风机编号	第1组	第2组	第3组	第4组	第1组	第2组	第3组	第4组
01	0.956	0.942	0.998	0.985	0.956	0.948	0.984	0.976
02	0.957	0.949	0.997	0.987	0.949	0.943	0.988	0.975
03	0.957	0.937	0.998	0.992	0.956	0.941	0.989	0.978
04	0.952	0.938	0.998	0.989	0.956	0.945	0.983	0.973
05	0.954	0.951	0.996	0.986	0.965	0.941	0.984	0.974
06	0.968	0.949	0.998	0.983	0.957	0.938	0.987	0.972
07	0.953	0.943	0.996	0.987	0.954	0.946	0.985	0.974
08	0.959	0.942	0.998	0.985	0.955	0.942	0.982	0.975
09	0.951	0.945	0.997	0.988	0.953	0.943	0.986	0.968
10	0.957	0.943	0.997	0.990	0.955	0.943	0.984	0.973

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