文献类型：期刊文献

英文题名：Acoustic-Emission-Driven Pipeline Leak Detection Using Wavelet Time-Frequency Maps and Inception-V3 Deep Network

作者：Zheng, Siqiang[1];Lu, Yu[2];Xu, Xuetong[1];Sun, Kai[1];Zhang, Lanzhu[1];Qian, Zhiqin[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai, Peoples R China;[2]Shenzhen Technol Univ, Sch Artificial Intelligence, Shenzhen, Peoples R China

年份：2026

卷号：87

期号：3

外文期刊名：CMC-COMPUTERS MATERIALS & CONTINUA

收录：;EI(收录号：20261620523215);WOS:【SCI-EXPANDED(收录号:WOS:001745491900001)】；

基金：Funding Statement: This research was supported by the National Key R&D Program of China (2023YFC3010500) .

语种：英文

外文关键词：KEYWORDS: Pipeline leak detection; acoustic emission; wavelet analysis; inception-V3 network

摘要：Pipelines play a crucial role in chemical industrial production. However, due to long operating cycles, seal failures, and internal corrosion, hazardous chemical media are prone to leak, potentially leading to serious accidents such as explosions. To address the limitations of existing pipeline leak detection methods-specifically their insufficient recognition accuracy and poor robustness in noisy environments-this paper proposes an Acoustic Emission (AE)-driven leakage state recognition method based on wavelet time-frequency maps and the InceptionV3 deep network. First, a pipeline leak experimental platform was constructed, and AE signals were collected. The signals were denoised through wavelet decomposition reconstruction. Then, the continuous wavelet transform (CWT) was applied to perform time-frequency analysis of the AE signals, generating wavelet time-frequency maps as the dataset. Finally, a deep learning classification model based on Inception-V3 was developed to identify different pipeline leak states. Experimental results show that the proposed method achieves a recognition accuracy of 99.6%. Compared with other network models and feature-based support vector machine (SVM) models, this method exhibits superior robustness in high noise and high recognition accuracy under small leakage conditions, confirming its effectiveness and advantages in pipeline leak detection.