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Acoustic Emission-Based Pipeline Leak Detection and Size Identification Using a Customized One-Dimensional DenseNet.

Faisal Saleem¹, Zahoor Ahmad¹, Muhammad Farooq Siddique¹

¹Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea.

Sensors (Basel, Switzerland)

|February 26, 2025

Summary

This summary is machine-generated.

This study introduces an advanced acoustic emission (AE) pipeline monitoring system using Empirical Wavelet Transform (EWT) and DenseNet deep learning for accurate leak detection and size classification, significantly improving industrial safety.

Keywords:

DenseNet acoustic emission deep learning empirical wavelet transform one-dimensional pipeline leakage

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Area of Science:

Engineering
Signal Processing
Artificial Intelligence

Background:

Industrial pipeline integrity relies on effective leak detection.
Traditional methods face limitations in noise sensitivity, adaptability, and computational cost.
Real-time monitoring requires robust and efficient leak identification solutions.

Purpose of the Study:

To develop a novel acoustic emission (AE)-based approach for precise pipeline leak detection and size classification.
To overcome the limitations of traditional monitoring techniques.
To enhance the safety and longevity of industrial pipelines.

Main Methods:

Utilized Empirical Wavelet Transform (EWT) for adaptive frequency decomposition and signal segmentation.
Applied adaptive thresholding and denoising to improve signal quality.
Employed a customized one-dimensional DenseNet deep learning model for feature extraction and classification.

Main Results:

Achieved an exceptional leak detection accuracy of 99.76% on real-world AE data.
Demonstrated reliable differentiation between normal operation and various leak severities.
Showcased reduced computational costs with robust performance across diverse environments.

Conclusions:

The proposed AE-based method with EWT and DenseNet offers a highly accurate and efficient solution for pipeline leak monitoring.
This approach enhances operational safety and integrity by enabling precise leak detection and severity classification.
The technique is adaptable to various operating conditions, offering a significant advancement over traditional methods.