# Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
## 6 Conclusions  
This study introduced an innovative approach for pipeline leak detection using advanced deep learning (DL) techniques. AE signals were collected from a pipeline system and transformed into CWT images to capture essential time-frequency features. A hybrid DL framework, integrating CNN and LSTM models, was developed to extract both spatial and temporal features effectively from these images. To further enhance feature relevance and classification accuracy, a Genetic Algorithm (GA) was employed for feature selection, ensuring that only the most discriminative features were retained. These optimized features were then fed into a fully connected layer for pipeline health classification. The proposed method demonstrated outstanding performance, achieving an impressive accuracy of 99.69% in leak detection. This highlights its robustness, reliability, and superiority over traditional approaches. The scientific significance of this research lies in its seamless integration of AE signal processing, time-frequency analysis, and DL techniques, resulting in a highly accurate and scalable solution for pipeline leak detection. Furthermore, the approach holds significant practical value, particularly for industries relying on pipelines as critical infrastructure, offering a reliable and efficient tool for real-time monitoring and maintenance.  
Future work will address precise leak localization by developing methods such as accurately extracting leak-related AE events and implementing time difference of arrival techniques. Building on this, the integration of hydraulic behavior analysis will be explored, incorporating physical models like Bernoulli's principle and pressure-loss equations. These advancements will enable a comprehensive framework that not only detects leaks with high accuracy but also localizes them precisely while considering the hydraulic dynamics of pipeline systems. Together, these developments aim to enhance operational safety, reduce environmental and economic impacts, and contribute to a broader understanding of pipeline health monitoring.  
Conceptualization, F.S., Z.A. and [PERSON]-M.K.; methodology, F.S., Z.A. and [PERSON]-M.K.; validation, F.S., Z.A. and [PERSON]-[PERSON]; formal analysis, F.S., Z.A. and [PERSON]-[PERSON]; resources, F.S., Z.A. and [PERSON]-[PERSON]; writing--original draft preparation, F.S., Z.A. and [PERSON]-M.K.; writing--review and editing, J.-M.K.; visualization, F.S., Z.A. and [PERSON]-M.K.; project administration, J.-M.K.; funding acquisition, J.-M.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korean government (MOTIE) ('RS-2023-00232515', 'Development of life prediction safety technology and hydrogen embtitlement estimation of LNG pipe mixed hydrogen'). This work was also supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korean government (MOTIE) ('RS-2024-00449107', 'Development of Flexible Pipe and Connector for Hydrogen Gas').  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company PD Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.