# Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
## 1 Introduction  
Pipelines are one of the most efficient and economical methods of transporting gases and liquids nowadays. While alternatives such as road tankers, rail cars, LNG ships, and compressed gas cylinders exist, they are often constrained by limited capacity, higher operational costs, and increased safety risks. Pipelines, in contrast, offer unmatched efficiency, cost-effectiveness, and safety. They are capable of handling large-scale, continuous transportation over long distances with minimal environmental impact, making them the preferred choice in modern infrastructure for oil and gas transportation [1]. Despite their advantages, pipelines are subjected to leaks caused by corrosion, earthquakes, mechanical cracks, environmental factors and material defects [2]. These leaks can have severe consequences, such as pollution, and public safety hazards, including economic losses and resource wastage [3, 4]. It is alarming to note that 46% of pipeline leak incidents globally result in casualties. For instance, a diesel pipeline leak in Guizhou, China, in 2020 resulted in a 1.5 million RMB loss and widespread environmental pollution [5]. Similarly, a petroleum pipeline explosion in Hidalgo, Mexico, caused by a leak, led to over 120 deaths and numerous injuries [6]. These examples underscore the urgent need for effective leak-detection systems [7, 8].  
Leak detection strategies have evolved over the decades. Traditional methods such as visual inspection, pressure monitoring, and acoustic-based methods were initially employed. However, these techniques were limited by delayed detection times, operator dependency, and the inability to pinpoint leaks in real time. These challenges led to the adoption of advanced approaches utilizing AE signals and machine learning, which now focus on real-time leak detection and precise localization. Real-time leak detection systems continuously monitor pipelines for signals such as pressure fluctuations, acoustic anomalies, or vibration changes [9]. These systems enable immediate detection and corrective actions, significantly reducing the environmental, economic, and safety impacts of leaks. By eliminating delays and ensuring continuous surveillance, real-time systems enhance pipeline reliability and operational efficiency, making them indispensable in modern infrastructure. The pipeline industry now focuses on cost-effective methods for repairing minor leaks, replacing encapsulating collars and clamps rather than replacing entire sections [10; 11]. Thus, intelligent leak detection methods are necessary for minimizing maintenance costs [12; 13]. Recent advancements in machine learning and artificial intelligence have greatly improved the efficiency and accuracy of pipeline leak detection [14; 15]. Techniques such as vibration-based methods, pressure wave techniques, time-domain reflection methods, and AE technology have been developed to ensure pipeline leak detection reliability [16; 17]. AE technology is particularly notable for its high sensitivity, easy installation, and real-time leak-detection capabilities [18]. Various studies have demonstrated the potential of AE technology in this domain, and developed the use of AE technology for detecting the beginning of cracks in pipelines [19; 20].