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| Corrosion Thickness Loss Rate Data Enhancement Based on a Small Sample of GAN |
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| DOI：10.7643/issn.1672-9242.2023.01.020 |
| KeyWord:corrosion thickness loss rate small sample generative adversarial networks data enhancement dimensionality reduction analysis sample distribution |
| Author | Institution |
| ZHOU Jun-yan |
Southwest Institute of Technology and Engineering, Chongqing , China |
| WANG Jing-cheng |
Southwest Institute of Technology and Engineering, Chongqing , China |
| YANG Xiao-kui |
Southwest Institute of Technology and Engineering, Chongqing , China |
| SHU Chang |
Southwest Institute of Technology and Engineering, Chongqing , China |
| WANG Jin-mei |
Southwest Institute of Technology and Engineering, Chongqing , China |
| ZHANG Chen |
Southwest Institute of Technology and Engineering, Chongqing , China |
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| Abstract: |
| The work aims to conduct data enhancement on the corrosion thickness loss rate of small samples to achieve data expansion, improve the prediction accuracy of the subsequent analysis model, reduce the degree of overfitting and improve the generalization ability of the model. The Generative Adversarial Network (GAN) was used to expand the corrosion thickness loss rate data and make the data distribution more comprehensive. Dimensionality reduction visual analysis on the generated data was conducted. The distribution of generated data and original data samples was explored. The rationality of data enhancement was analyzed. In addition, the analysis and prediction ability and generalization ability were evaluated from the perspectives of multiple algorithm models and multiple evaluation indicators. The generated data filled in the weak link of the original data in the sample space distribution. After adding the generated data, the average MSE obtained by each machine learning algorithm model was 61.72% to 91.74% of the result without the generated data, and the Pearson average was 99.01% to 113.64 %. The prediction accuracy was improved. The results were more relevant. And the model generalization ability was enhanced. GAN can effectively enhance the corrosion thickness loss rate data of small samples. Data expansion provides positive support for analysis and prediction. The generated data should not be more than the original data to prevent disturbing the distribution of training samples. At the same time, there are problems with limited diversity of generated data. |
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