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Volume 14
Issue 22
10.3390/app142210339
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Article

Spatio-Temporal Feature Extraction for Pipeline Leak Detection in Smart Cities Using Acoustic Emission Signals: A One-Dimensional Hybrid Convolutional Neural Network–Long Short-Term Memory Approach

by
Saif Ullah
1,
Niamat Ullah
1,
Muhammad Farooq Siddique
1,
Zahoor Ahmad
1 and
Jong-Myon Kim
1,2,*
1
Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
2
Prognosis and Diagnostic Technologies Co., Ltd., Ulsan 44610, Republic of Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(22), 10339; https://doi.org/10.3390/app142210339
Submission received: 19 October 2024 / Revised: 7 November 2024 / Accepted: 7 November 2024 / Published: 10 November 2024
(This article belongs to the Special Issue Application and Simulation of Fluid Dynamics in Pipeline Systems)
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Abstract

Pipeline leakage represents a critical challenge in smart cities and various industries, leading to severe economic, environmental, and safety consequences. Early detection of leaks is essential for overcoming these risks and ensuring the safe operation of pipeline systems. In this study, a hybrid convolutional neural network–long short-term memory (CNN-LSTM) model for pipeline leak detection that uses acoustic emission signals was designed. In this model, acoustic emission signals are initially preprocessed using a Savitzky–Golay filter to reduce noise. The filtered signals are input into the hybrid model, where spatial features are extracted using a CNN. The features are then passed to an LSTM network, which extracts temporal features from the signals. Based on these features, the presence or absence of a leakage is determined. The performance of the proposed model was compared with two alternative approaches: a method that employs combined features from the time domain and LSTM and a bidirectional gated recurrent unit model. The proposed approach demonstrated superior performance, as evidenced by lower validation loss, higher validation accuracy, enhanced confusion matrices, and improved t-distributed stochastic neighbor embedding plots compared to the other models when tested on industrial data. The findings indicate that the proposed model is more effective in accurately detecting pipeline leaks, offering a promising solution for enhancing smart cities and industrial safety.
Keywords: acoustic emission; leak detection; Savitzky–Golay; convolutional neural network; long short-term memory; hybrid model acoustic emission; leak detection; Savitzky–Golay; convolutional neural network; long short-term memory; hybrid model

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MDPI and ACS Style

Ullah, S.; Ullah, N.; Siddique, M.F.; Ahmad, Z.; Kim, J.-M. Spatio-Temporal Feature Extraction for Pipeline Leak Detection in Smart Cities Using Acoustic Emission Signals: A One-Dimensional Hybrid Convolutional Neural Network–Long Short-Term Memory Approach. Appl. Sci. 2024, 14, 10339. https://doi.org/10.3390/app142210339

AMA Style

Ullah S, Ullah N, Siddique MF, Ahmad Z, Kim J-M. Spatio-Temporal Feature Extraction for Pipeline Leak Detection in Smart Cities Using Acoustic Emission Signals: A One-Dimensional Hybrid Convolutional Neural Network–Long Short-Term Memory Approach. Applied Sciences. 2024; 14(22):10339. https://doi.org/10.3390/app142210339

Chicago/Turabian Style

Ullah, Saif, Niamat Ullah, Muhammad Farooq Siddique, Zahoor Ahmad, and Jong-Myon Kim. 2024. "Spatio-Temporal Feature Extraction for Pipeline Leak Detection in Smart Cities Using Acoustic Emission Signals: A One-Dimensional Hybrid Convolutional Neural Network–Long Short-Term Memory Approach" Applied Sciences 14, no. 22: 10339. https://doi.org/10.3390/app142210339

APA Style

Ullah, S., Ullah, N., Siddique, M. F., Ahmad, Z., & Kim, J.-M. (2024). Spatio-Temporal Feature Extraction for Pipeline Leak Detection in Smart Cities Using Acoustic Emission Signals: A One-Dimensional Hybrid Convolutional Neural Network–Long Short-Term Memory Approach. Applied Sciences, 14(22), 10339. https://doi.org/10.3390/app142210339

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