Pipeline Internal Surface Detection based on Convolutional Neural Networks and Code Wave Time-Frequency Transform

Abstract

This study introduces an innovative approach that integrates principles of coda wave interferometry, wavelet transform, convolutional neural networks, and deep learning for the analysis and detection of contaminants within pipelines. The primary objective of this method is to identify various types of contaminants, enhance contamination detection sensitivity and accuracy, thereby providing an effective solution for monitoring and remediation in the oil and gas pipeline transportation industry. The findings of this research offer valuable insights and serve as a significant reference for improving pipeline cleaning practices. Further optimization can be explored through enhancements in the model architecture, the incorporation of data augmentation techniques, and the exploration of alternative training strategies. However, the performance of this method still requires further study and improvement. The study encompasses the following key aspects: Firstly, ultrasonic analysis is employed to characterize the structural properties of contaminants, enabling the extraction of crucial structural features and performance parameters through the analysis of ultrasonic echo signals. Secondly, wavelet transform is applied to conduct comprehensive time-frequency analysis of the ultrasonic echo signals, facilitating the capture of distinctive features associated with contaminants across different frequency bands and time scales. Thirdly, convolutional neural networks, specifically leveraging the VGG-16 model, are used to extract informative features from the ultrasonic signals and perform classification. Through rigorous training and fine-tuning processes, the model achieves precise identification of contaminant types and estimation of their thickness.