Minor class-based status detection for pipeline network using enhanced generative adversarial networks

Abstract

For the operational status detection of a pipeline network, it is essential to obtain enough samples of each class during the network operation. However, the phenomenon of few actual leak samples give rise to the imbalanced dataset problem. To address this issue, this paper proposes a minor class-based status detection method using enhanced generative adversarial networks (enhanced-GANs). First, a generative model with a U-net structure is established to generate the required samples with the modified normal samples, and the L1 loss and L2 loss functions are utilized to update the network parameters. Then, output results and extracted features regarding different layers of discriminative network are added in the generative network loss function to improve the quality of the generated samples. Furthermore, based on the hidden features extracted by the trained discriminative network, an enhanced dual judgment scheme is proposed to improve the status detection performance. Finally, extensive experiments are carried out to evaluate the proposed method with the dataset collected from a practical pipeline network. The experiment results show that the proposed method can not only provide enough leak samples but also effectively improves the status detection accuracy for a pipeline network.