A GAN noise modeling based blind denoising method for guided waves

In the detection using guided waves, the signal often carries a high level of non-Gaussian noise. The traditional denoising method cannot estimate and use the prior information of the signal, which leads to poor denoising effect. To tackle this problem, this paper proposed a denoising network based on the combination of generative adversarial network (GAN) and autoencoder (AE). First, GAN is used to estimate the distribution characteristics of the extracted noise and generate samples. Second, according to the characteristics of the guided wave, a pair of datasets are generated to train DAE network. The trained denoising AE has strong robustness. As a result, the proposed GAN-AE based denoiser (GAD) can effectively can effectively reduce the noise level and has the ability to accurately recover the peak time of the wave packet. In particular, the proposed method significantly outperforms conventional denoising methods in low signal-to-noise (SNR) conditions.