Subdomain-Alignment Data Augmentation for Pipeline Fault Diagnosis: An Adversarial Self-Attention Network
Chuang Wang, Zidong Wang, Lifeng Ma, Hongli Dong, Weiguo Sheng
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.