Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
This study introduces an advanced deep-learning framework for the real-time detection of pipeline leaks in smart city infrastructure. The methodology transforms acoustic emission (AE) signals from the time domain into scalogram images using continuous wavelet transform (CWT) to enhance leak-related...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/185 |
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| author | Faisal Saleem Zahoor Ahmad Jong-Myon Kim |
| author_facet | Faisal Saleem Zahoor Ahmad Jong-Myon Kim |
| author_sort | Faisal Saleem |
| collection | DOAJ |
| description | This study introduces an advanced deep-learning framework for the real-time detection of pipeline leaks in smart city infrastructure. The methodology transforms acoustic emission (AE) signals from the time domain into scalogram images using continuous wavelet transform (CWT) to enhance leak-related features. A Gaussian filter minimizes background noise and clarifies these features further. The core of the framework combines convolutional neural networks (CNNs) with long short-term memory (LSTM), ensuring a comprehensive examination of both spatial and temporal features of AE signals. A genetic algorithm (GA) optimizes the neural network by isolating the most important features for leak detection. The final classification stage uses a fully connected neural network to categorize pipeline health conditions as either ‘leak’ or ‘non-leak’. Experimental validation on real-world pipeline data demonstrated the framework’s efficacy, achieving accuracy rates of 99.69%. This approach significantly advances smart city capabilities in pipeline monitoring and maintenance, offering a durable and scalable solution for proactive infrastructure management. |
| format | Article |
| id | doaj-art-7166601c30674c2497a269c835aceda3 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-7166601c30674c2497a269c835aceda32025-01-10T13:14:43ZengMDPI AGApplied Sciences2076-34172024-12-0115118510.3390/app15010185Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission SignalsFaisal Saleem0Zahoor Ahmad1Jong-Myon Kim2Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of KoreaDepartment of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of KoreaDepartment of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of KoreaThis study introduces an advanced deep-learning framework for the real-time detection of pipeline leaks in smart city infrastructure. The methodology transforms acoustic emission (AE) signals from the time domain into scalogram images using continuous wavelet transform (CWT) to enhance leak-related features. A Gaussian filter minimizes background noise and clarifies these features further. The core of the framework combines convolutional neural networks (CNNs) with long short-term memory (LSTM), ensuring a comprehensive examination of both spatial and temporal features of AE signals. A genetic algorithm (GA) optimizes the neural network by isolating the most important features for leak detection. The final classification stage uses a fully connected neural network to categorize pipeline health conditions as either ‘leak’ or ‘non-leak’. Experimental validation on real-world pipeline data demonstrated the framework’s efficacy, achieving accuracy rates of 99.69%. This approach significantly advances smart city capabilities in pipeline monitoring and maintenance, offering a durable and scalable solution for proactive infrastructure management.https://www.mdpi.com/2076-3417/15/1/185acoustic emissioncontinuous wavelet transformpipeline leakageconvolutional neural networksdeep learning |
| spellingShingle | Faisal Saleem Zahoor Ahmad Jong-Myon Kim Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals Applied Sciences acoustic emission continuous wavelet transform pipeline leakage convolutional neural networks deep learning |
| title | Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals |
| title_full | Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals |
| title_fullStr | Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals |
| title_full_unstemmed | Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals |
| title_short | Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals |
| title_sort | real time pipeline leak detection a hybrid deep learning approach using acoustic emission signals |
| topic | acoustic emission continuous wavelet transform pipeline leakage convolutional neural networks deep learning |
| url | https://www.mdpi.com/2076-3417/15/1/185 |
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