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. 2024 Mar 1;17(5):1142.
doi: 10.3390/ma17051142.

Data Augmentation of a Corrosion Dataset for Defect Growth Prediction of Pipelines Using Conditional Tabular Generative Adversarial Networks

Haonan Ma  1 , Mengying Geng  1 , Fan Wang  1 , Wenyue Zheng  1 , Yibo Ai  1   2 , Weidong Zhang  1
Affiliations

Data Augmentation of a Corrosion Dataset for Defect Growth Prediction of Pipelines Using Conditional Tabular Generative Adversarial Networks

Haonan Ma et al. Materials (Basel). .

Abstract

Due to corrosion characteristics, there are data scarcity and uneven distribution in corrosion datasets, and collecting high-quality data is time-consuming and sometimes difficult. Therefore, this work introduces a novel data augmentation strategy using a conditional tabular generative adversarial network (CTGAN) for enhancing corrosion datasets of pipelines. Firstly, the corrosion dataset is subjected to data cleaning and variable correlation analysis. The CTGAN is then used to generate external environmental factors as input variables for corrosion growth prediction, and a hybrid model based on machine learning is employed to generate corrosion depth as an output variable. The fake data are merged with the original data to form the synthetic dataset. Finally, the proposed data augmentation strategy is verified by analyzing the synthetic dataset using different visualization methods and evaluation indicators. The results show that the synthetic and original datasets have similar distributions, and the data augmentation strategy can learn the distribution of real corrosion data and sample fake data that are highly similar to the real data. Predictive models trained on the synthetic dataset perform better than predictive models trained using only the original dataset. In comparative tests, the proposed strategy outperformed other data generation methods.

Keywords: CTGAN; corroded pipeline; corrosion depth; data augmentation; machine learning.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
The violin plot of the modified dataset.
Figure 2
Figure 2
The Spearman correlation coefficient between variables in soil properties.
Figure 3
Figure 3
The proposed data augmentation strategy.
Figure 4
Figure 4
The structure of GAN.
Figure 5
Figure 5
Histogram of the frequency distribution of the variable.
Figure 6
Figure 6
Heat map of the Spearman correlation coefficient of the synthetic dataset.
Figure 7
Figure 7
The principal components of the original dataset and synthetic dataset.
Figure 8
Figure 8
Comparison of predicted and real corrosion depth using Model_Syn and Model_Ori.
Figure 9
Figure 9
The framework for comparing with other data generation methods.
Figure 10
Figure 10
Taylor diagrams for comparison of models.
See this image and copyright information in PMC

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