Pipeline leak detection based on generative adversarial networks under small samples

Abstract

During the actual industrial process, it is challenging to obtain samples of oil and gas pipeline leaks resulting in the scarcity of training data samples suitable for fault diagnosis. In order to tackle this issue, this paper suggests a method a Recursive Generalization self-attention generative adversarial network (RAGAN) aided by Wasserstein distance with gradient penalty. The initial step involves applying the short time Fourier transform to the acoustic signal of oil and gas pipeline leakage, treating it as real data. Subsequently, both the real data and random noise following a Gaussian distribution are fed into the generator. The output is utilised as a pseudo sample. The Wasserstein distance of the distribution of real data and fake samples is introduced as a loss term in the discriminator, and a gradient penalty is added. Finally, the network optimizes the parameters through back propagation until Nash equilibrium. PSNR and SSIM are used as sample reliability evaluation. The results show that the fake samples have high similarity with the real samples, which can be used to expand small sample data. Moreover, extending pseudo samples to small sample data sets can effectively improve the performance of fault diagnosis.