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Subdomain-Alignment Data Augmentation for Pipeline Fault Diagnosis: An Adversarial Self-Attention Network
- Source :
- IEEE Transactions on Industrial Informatics; February 2024, Vol. 20 Issue: 2 p1374-1384, 11p
- Publication Year :
- 2024
-
Abstract
- Data augmentation (DA) has the potential to address the issue of imbalanced and insufficient datasets (I&ID) in pipeline fault diagnosis. However, the majority of existing DA methods for time series are inspired by computer vision techniques, ignoring the temporal dynamic properties and fine-grained fault features, which leads to limited performance of the augmentation. To tackle this problem, we introduce a novel DA approach called the subdomain-alignment adversarial self-attention network (SA-ASN), which takes into account both temporal association and semantic correlation. Our approach features a novel temporal association learning (TAL) mechanism, which transfers temporal information from the discriminator to the generator via a customized knowledge-sharing structure, improving the reliability of synthetic long-range associations. Additionally, we introduce a prototype-assisted subdomain alignment (PASA) strategy that forms a hierarchical structure in the synthetic dataset by incorporating local semantic correlation into the model training. With the support of TAL and PASA, our SA-ASN algorithm enhances the authenticity of temporal structure at the instance level and improves the discriminability of fault features at the category level. Our experimental results show that the SA-ASN algorithm provides a more diverse and accurate augmentation of pipeline data. The effectiveness of our SA-ASN algorithm encourages the use of data-driven diagnostic models in complex real-world oilfield pipeline networks.
Details
- Language :
- English
- ISSN :
- 15513203
- Volume :
- 20
- Issue :
- 2
- Database :
- Supplemental Index
- Journal :
- IEEE Transactions on Industrial Informatics
- Publication Type :
- Periodical
- Accession number :
- ejs65300942
- Full Text :
- https://doi.org/10.1109/TII.2023.3275701