Acoustic-Emission–Driven Pipeline Leak Detection Using Wavelet Time–Frequency Maps and Inception-V3 Deep Network
Siqiang Zheng1,#, Yu Lu2,#, Xuetong Xu1, Kai Sun1, Lanzhu Zhang1,*, Zhiqin Qian1,*
1 School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China
2 School of Artificial Intelligence, Shenzhen Technology University, Shenzhen, China
* Corresponding Author: Lanzhu Zhang. Email: 
; Zhiqin Qian. Email: 
# Siqiang Zheng and Yu Lu contributed equally to this work
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.076059
Received 13 November 2025; Accepted 26 February 2026; Published online 13 March 2026
Abstract
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 Inception-V3 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.
Keywords
Pipeline leak detection; acoustic emission; wavelet analysis; inception-V3 network
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