# Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
## 1 Introduction
### Related Research Work  
A pipeline leak produces elastic energy, leading to AE events. These occurrences are recorded as AE hits (AEHs) by AE sensors on the pipeline's surface. The variations in the AE signal caused by these AEHs are essential for detecting leaks [21]. Researchers have concentrated on pattern recognition and feature extraction techniques to make effective use of these signal variations [22]. Techniques for detecting pipeline leaks using intelligent pattern recognition can be classified into three main types: frequency domain (FD), temporal domain (TD), and time-frequency domain (TFD). [PERSON] et al. [23] used principal component analysis and pre-processed AE signals to obtain low-dimensional discriminant features before using TD statistical features for leak identification. An artificial neural network (ANN) was applied in conjunction with the amplitude of the TD AE signal to detect leaks [24]. AE signals are complicated and non-stationary; stationary signals are better for FD analysis. Thus, to extract useful features from non-stationary AE data, TFD techniques like wavelet transform and empirical mode decomposition are used [25]. With the use of pattern recognition methods like SVDD, ANN, and fuzzy-SVDD, these features assist in the identification of pipeline health issues. However, TFD preprocessing involves significant computational costs, and choosing the appropriate base wavelet for wavelet transforms requires experimental validation [26]. AEH characteristics have demonstrated good results compared to traditional extracted characteristics from AE signals in FD, TD, and TFD for various approaches [27]. However, AEH features have limitations due to multiple AEH sources, such as fluid pressure, background noise, increased vibrations, and leaks, reducing their sensitivity to leak identification [28]. Extracting leak-related characteristics from the AE signal is the first step in the pipeline leak diagnosis system. DL techniques have an advantage over typical machine learning techniques in their ability to analyze complex data [29]. For pattern recognition tasks, DL techniques independently extract discriminative, meaningful information from complex data. CNN, neural auto-encoders, recurrent neural networks, and deep belief networks are the most popular DL techniques for defect identification. CNNs use local representative fields, share weights within the network, and use special domain subsampling to minimize the danger of overfitting and enable low computing complexity. Moreover, CNNs have shown promising pattern recognition in pipelines, centrifugal pumps, and bearing failure diagnosis [30,31]. Various studies have demonstrated the potential of AE technology in leak detection and have developed methods for detecting cracks and other anomalies in pipelines. Techniques such as principal component analysis, neural networks, and wavelet transforms have been explored for feature extraction and pattern recognition. Deep learning has gained significant attention for its ability to solve complex problems in various domains. For example, CNN-based transfer learning models have been successfully applied to classify microseismic event waveforms [32,33]. This study highlights the effectiveness of transfer learning in handling data scarcity, which can be adapted to pipeline leak detection scenarios with limited leak data by utilizing pre-trained models. A hybrid deep learning and transfer learning approach for aerosol retrieval [34] showcased the potential of integrating domain-specific preprocessing with deep learning frameworks. Similarly, our work incorporates domain-specific preprocessing CWT scalograms and Gaussian filtering to enhance signal clarity and feature extraction. Attention U-Net architectures for self-potential inversion tasks [35] demonstrated how attention mechanisms can enhance model focus on critical data regions. This inspires potential future extensions of our pipeline monitoring framework to include attention layers for focusing on key time-frequency regions of AE scalograms.