Application of Acoustic Signal Hybrid Filtering with 1D-CNN for Fault Diagnosis of Natural Gas Pipeline Leakage
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 15, Issue 3
Abstract
In acoustic signal–based natural gas pipeline leak detection, it is a technical difficulty to effectively remove the strong background noise hidden in the acoustic signal. Unfortunately, existing techniques usually study denoising in isolation from the model, and do not consider the synergistic effect of integrating denoising with the model to update filtering parameters adaptively, which limits the performance development of the diagnostic model. To solve this problem, this paper proposes a fault diagnosis method that incorporates acoustic signal hybrid filtering and a one-dimensional convolutional neural network (1D-CNN) to jointly update the denoising parameters. Firstly, the acoustic signal is sampled at a fixed frequency by the sensor unit. Secondly, a hybrid filtering method with low-frequency characteristics is designed, which integrates low-pass filtering and median filtering to eliminate background noise. Finally, an adaptive fault diagnosis network that fuses acoustic signal denoising with 1D-CNN feature extraction is developed. The network updates the filtering parameters during training based on the model recognition accuracy, so that the denoising parameters and model weights change dynamically. The experimental results show that the method is capable of performing adaptive parameter search with 97.07% identification accuracy of fault type.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (No. KJQN202200531), the Foundation Project of Chongqing Normal University (No. 22XLB014), and the Graduate Innovation Project of Chongqing University of Science and Technology (No. YKJCX2120406).
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Journal of Pipeline Systems Engineering and Practice
Volume 15 • Issue 3 • August 2024
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© 2024 American Society of Civil Engineers.
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Received: Oct 5, 2023
Accepted: Mar 18, 2024
Published online: Jun 10, 2024
Published in print: Aug 1, 2024
Discussion open until: Nov 10, 2024
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