Tracking and Localization Method of Pipeline Pigs Based on a Noise Reduction Autoencoder

Abstract

In the pigging operation of an oil pipeline, the traditional pigging localization method can only achieve fixed-point detection and cannot track continuously. To ensure the normal operation of the pigging operation and track and locate the pig in a timely manner, a tracking and localization method of an oil pipeline pig based on a noise reduction autoencoder is proposed. First, using the advantages of U-Net (U-shaped Convolutional Neural Network) multi-scale feature extraction, combined with coding and residual-dense blocks, U-Net combined with Residual Dense Block U-shaped Network (RDBU) was proposed for noise reduction, and then real-time localization was calculated out based on the negative pressure wave localization formula of pigs. The experimental results show that, compared with the traditional method, the SNR of the RDBU is increased by 0.9dB and the root mean square error is reduced by 14.92%. The denoising algorithm in this paper can effectively eliminate the noise of the negative pressure wave signal of pigs.