Bearing Faults Diagnosis Method Based on Stacked Auto-Encoder With Graph Regularization for Wind Turbines

doi:10.12096/j.2096-4528.pgt.23175

Abstract

Abstract:

Objectives In order to solve the problems of low efficiency of fault feature extraction, inaccurate feature representation, and difficulty in adapting existing methods to complex signal requirements in wind turbine bearing fault diagnosis, a fault diagnosis method based on graph regularization was proposed. The method helps to improve the analysis ability of vibration signals, thereby achieving accurate classification and reliable diagnosis of different fault types. Methods The technology based on graph regularization auto-encoder was adopted, combined with the idea of graph embedding, to guide the stack-supervised auto-encoder to carry out feature extraction. In the fault feature extraction stage, the diagnostic signal was first graphically represented, and then graph regularization terms were added to the stacked auto-encoder to ensure that the embedded low-dimensional features maintain the manifold structure, thereby extracting complex geometric features deep in the data. Results The extracted features can accurately classify different fault types, showing significant advantages in fault feature capture. Experimental results show that this method has higher diagnosis accuracy and reliability in the application of actual wind farm data, and effectively improves the extraction efficiency and classification accuracy of fault features. Conclusions The proposed method has shown significant effectiveness and advantages in the field of wind power bearing fault diagnosis, and provides reliable technical support for practical applications.

Key words: wind turbine, bearing, fault diagnosis, graph regularization, auto-encoder, graph embedding, feature extraction, vibration signal

CLC Number:

TK 83

Zhan LIU, Jianxun LIU, Yanyang BAO, Dazi LI. Bearing Faults Diagnosis Method Based on Stacked Auto-Encoder With Graph Regularization for Wind Turbines[J]. Power Generation Technology, 2024, 45(6): 1146-1152.

Figures/Tables 8

Fig. 1 Autoencoder network architecture

Fig. 2 Graphical representations of fault characteristics

Tab. 1 Performance comparison of different wavelet functions

小波函数	MSE	SNR	PSNR
Coiflet	15.463 2	11.944 8	12.513 6
Meyer	16.485 9	11.666 7	12.235 4
Daubechies	17.040 3	11.523 0	12.091 7
Symlet	17.018 2	11.528 7	12.097 4

Tab. 2 Characteristic parameters

时域特征	频域特征
$p 1 = ∑ n = 1 N x (n) N$	$p 12 = ∑ k = 1 K s (k) K$
$P 2 = ∑ n = 1 N [x (n) - p 1] 2 N - 1$	$p 13 = ∑ k = 1 K [s (k) - p 12] 2 K - 1$
$p 3 = (∑ n = 1 N x (n) N) 2$	$p 14 = ∑ k = 1 K [s (k) - p 12] 3 K (p 13) 3$
$p 4 = ∑ n = 1 N [x (n)] 2 N$	$p 15 = ∑ k = 1 K [s (k) - p 12] 4 K p 132$
$p 5 = m a x x (n)$	$p 16 = ∑ k = 1 K f k s (k) ∑ k = 1 K s (k)$
$p 6 = ∑ n = 1 N [x (n) - p 1] 3 (N - 1) p 23$	$p 17 = ∑ k = 1 K (f k - P 16) 2 s (k) K$
$p 7 = ∑ n = 1 N [x (n) - p 1] 4 (N - 1) p 24$	$p 18 = ∑ k = 1 K f k 2 s (k) ∑ k = 1 K s s (k)$
$p 8 = p 5 p 4$	$p 19 = ∑ k = 1 K f k 4 s (k) ∑ k = 1 K f k 2 s (k)$
$p 9 = p 5 p 3$	$p 20 = ∑ k = 1 K f k 2 s (k) ∑ k = 1 K s (k) ∑ k = 1 K f k 4 (k)$
$p 10 = p 4 1 N ∑ n = 1 N x (n)$	$p 21 = p 17 p 16$
$p 11 = p 5 1 N ∑ n = 1 N x (n)$	$p 22 = ∑ k = 1 K (f k - p 16) 3 s (k) K p 173$
	$p 23 = ∑ k = 1 K (f k - p 16) 4 s (k) K p 174$
	$p 24 = ∑ k = 1 K (f k - p 16) 1 / 2 s (k) K p 17$

Tab. 2 Characteristic parameters

时域特征	频域特征
$p 1 = ∑ n = 1 N x (n) N$	$p 12 = ∑ k = 1 K s (k) K$
$P 2 = ∑ n = 1 N [x (n) - p 1] 2 N - 1$	$p 13 = ∑ k = 1 K [s (k) - p 12] 2 K - 1$
$p 3 = (∑ n = 1 N x (n) N) 2$	$p 14 = ∑ k = 1 K [s (k) - p 12] 3 K (p 13) 3$
$p 4 = ∑ n = 1 N [x (n)] 2 N$	$p 15 = ∑ k = 1 K [s (k) - p 12] 4 K p 132$
$p 5 = m a x x (n)$	$p 16 = ∑ k = 1 K f k s (k) ∑ k = 1 K s (k)$
$p 6 = ∑ n = 1 N [x (n) - p 1] 3 (N - 1) p 23$	$p 17 = ∑ k = 1 K (f k - P 16) 2 s (k) K$
$p 7 = ∑ n = 1 N [x (n) - p 1] 4 (N - 1) p 24$	$p 18 = ∑ k = 1 K f k 2 s (k) ∑ k = 1 K s s (k)$
$p 8 = p 5 p 4$	$p 19 = ∑ k = 1 K f k 4 s (k) ∑ k = 1 K f k 2 s (k)$
$p 9 = p 5 p 3$	$p 20 = ∑ k = 1 K f k 2 s (k) ∑ k = 1 K s (k) ∑ k = 1 K f k 4 (k)$
$p 10 = p 4 1 N ∑ n = 1 N x (n)$	$p 21 = p 17 p 16$
$p 11 = p 5 1 N ∑ n = 1 N x (n)$	$p 22 = ∑ k = 1 K (f k - p 16) 3 s (k) K p 173$
	$p 23 = ∑ k = 1 K (f k - p 16) 4 s (k) K p 174$
	$p 24 = ∑ k = 1 K (f k - p 16) 1 / 2 s (k) K p 17$

Fig. 3 Comparison of time-domain signals before and after preprocessing of gearbox bearing data

Fig. 4 Visualization results of fault characteristics

Tab. 3 Cassification accuracy of bearing wear fault types using different feature extraction methods

测点位置	图正则化自编码器	传统时频域特征提取
主轴轴承	0.976	0.908
齿轮箱轴承	0.933	0.879
发电机轴承	0.949	0.913

Tab. 4 Classification accuracy of inner and outer ring damage fault types using different feature extraction methods

测点位置	图正则化自编码器	传统时频域特征提取
主轴轴承	0.953	0.900
齿轮箱轴承	0.978	0.853
发电机轴承	0.992	0.950

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