A Novel Transfer Learning Method Based on Conditional Variational Generative Adversarial Networks for Fault Diagnosis of Wind Turbine Gearboxes under Variable Working Conditions
Abstract
:1. Introduction
2. Conditional Variational Generative Adversarial Networks and Domain Adaptive Technology
2.1. Conditional Variational Generative Adversarial Networks (CVAE-GAN)
2.2. Domain Adaptive Technology (DA)
- (1)
- Sample adaptation: resampling samples in the source domain so that their distribution converges with the target domain distribution.
- (2)
- Feature adaptation: projecting the source and target domains into a common feature subspace.
- (3)
- Model adaption: modification of the source domain error function.
3. Transfer Learning Based on Conditional Variational Generative Adversarial Networks (TL-CVAE-GAN)
Algorithm 1. TL-CVAE-GAN |
Input: Input training data, , testing data, , classified model, fC. In the CVAE-GAN1 model: encoder network, fE1, decoder network, fDE1, generator network, fG1, and discriminator network, fD1. In the CVAE-GAN2 model: encoder network, fE2, generator network, fG2, discriminator network, fD2.The learning rate, lr. ########################Cycle 5 times #################### 1: For f from 0 to 4: ########################train CVAE-GAN1 model #################### 2: For each training epoch, do: 3: For each batch, do: 4: zi = fE1(xs1i, Speed1), xs1i’ = fDE1(zi), the mean value, usi, and variance, σsi, are obtained from zi, sample e from the random noise S. zsi = usi +σsi *e, xs2i’= fG1(zsi, Speed2), ds2i’= fD1(xs2i’), ds2i= fD1(xs2i) 5: Backward propagation by Equation (9). 6: end 7: save CVAE-GAN1 model #################### train CVAE-GAN2 model use MMD ####################### 8: download CVAE-GAN1 model. Use the parameters of the CVAE-GAN1 model as the initial parameters of CVAE-GAN2. 9: For each training, do: 10: For each batch, do: 11: zi = fE2(xt1i), zti = uti +σti *e, xt2i’= fG2(zti), 12: Backward propagation by Equation (12). 13: end 14: save CVAE-GAN2 model 15: lr = lr/2 16: if f > 0: 17: download the CVAE-GAN2 model. Use the parameters of the CVAE-GAN2 model as the initial parameters of CVAE-GAN1. 18: end ########### train classifier net use Tr and the generate data XT2′ ################# ########the input data is X = {(XS1, YS1), (XS2, YS2), (XT1, YT1), (XT2′, YT2)}########### 19: For each training, do: 20: For each batch, do: 21: yi’ = fC(xi) 22: Backward propagation by Equation (13). 23: end ###################### testing results and t-SNE ######################### 24: For the test set, calculate cTi = fC (Tei), calculate the accuracy, and draw the t-SNE diagram. Output: testing results. |
4. Case Analysis
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Conflicts of Interest
References
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Domain | Data | Work Condition | Known or Not |
---|---|---|---|
Source domain | XS1 | Speed1 | Data available |
XS2 | Speed2 | Data available | |
Target domain | XT1 | Speed1 | Data available |
XT2 | Speed2 | Data not available |
Fault Modes | Label | Speed (Hz) | Sampling Frequency | Number of Dataset |
---|---|---|---|---|
Normal | 0 | 38, 40, 43, 45 | 8192 Hz | 256 × 4 |
Cracked | 1 | 38, 40, 43, 45 | 8192 Hz | 256 × 4 |
Chipped | 2 | 38, 40, 43, 45 | 8192 Hz | 256 × 4 |
Missing | 3 | 43, 45 | 8192 Hz | 256 × 2 |
Wear | 4 | 43, 45 | 8192 Hz | 256 × 2 |
Eccentricity | 5 | 43, 45 | 8192 Hz | 256 × 2 |
Data | Label | Speed (Hz) | Number of Training Dataset | Number of Testing Dataset | |
---|---|---|---|---|---|
Source domain | XS1 | 0, 1, 2 | 43, 45 | 160 × 3 × 2 | 256 × 3 × 2 |
XS2 | 0, 1, 2 | 38, 40 | 160 × 3 × 2 | 256 × 3 × 2 | |
Target domain | XT1 | 3, 4, 5 | 43, 45 | 160 × 3 × 2 | 256 × 3 × 2 |
XT2 | 3, 4, 5 | 38, 40 | 0 | 256 × 3 × 2 |
Only the Training Set Trains the Classifier | Training Set and Generated Data together to Train the Classifier | Improved | |
---|---|---|---|
Classification accuracy | 77.8% | 99.1% | 21.3% |
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Liu, X.; Ma, H.; Liu, Y. A Novel Transfer Learning Method Based on Conditional Variational Generative Adversarial Networks for Fault Diagnosis of Wind Turbine Gearboxes under Variable Working Conditions. Sustainability 2022, 14, 5441. https://doi.org/10.3390/su14095441
Liu X, Ma H, Liu Y. A Novel Transfer Learning Method Based on Conditional Variational Generative Adversarial Networks for Fault Diagnosis of Wind Turbine Gearboxes under Variable Working Conditions. Sustainability. 2022; 14(9):5441. https://doi.org/10.3390/su14095441
Chicago/Turabian StyleLiu, Xiaobo, Haifei Ma, and Yibing Liu. 2022. "A Novel Transfer Learning Method Based on Conditional Variational Generative Adversarial Networks for Fault Diagnosis of Wind Turbine Gearboxes under Variable Working Conditions" Sustainability 14, no. 9: 5441. https://doi.org/10.3390/su14095441
APA StyleLiu, X., Ma, H., & Liu, Y. (2022). A Novel Transfer Learning Method Based on Conditional Variational Generative Adversarial Networks for Fault Diagnosis of Wind Turbine Gearboxes under Variable Working Conditions. Sustainability, 14(9), 5441. https://doi.org/10.3390/su14095441