## 1 Introduction
Therefore, TFD techniques, such as empirical mode decomposition and variational mode decomposition, are used to extract leak-related information from non-stationary AE signals. These TFD techniques preprocess the complex AE signals, allowing for the extraction of meaningful features. These features are then used to determine the health of the pipeline using pattern recognition techniques such as SVDD, fuzzy SVDD, and ANN [28; 29; 30; 31; 32]. However, preprocessing AE signals with TFD techniques is computationally expensive, and selecting the appropriate mother wavelet for transform requires experimental validation. Although empirical mode decomposition is a self-adaptive technique, various issues, such as mode mixing and extreme interpolation, make it less desirable for preprocessing pipeline AE signals. The features derived from AEHs, such as peak amplitude, AE burst counts, rise time, decay time, and average frequency, are important because they contain information related to the AE event caused by faults [33]. As a result, AEH features are independent of the overall AE signal distribution [34]. AEH features have shown superior performance compared to traditional features extracted from AE signals in the time, frequency, and time-frequency domains for various applications, including pipelines, concrete structures, and mooring chains [35; 36]. AEHs can either be a burst-type or continuous-type: burst-type AEHs are rapid sequences of hits, while continuous-type AEs are the noise generated by the AE source. Burst-type AEHs are easier to distinguish from background noise compared to continuous type AEHs. To filter out noise from the AE signal, the proposed method utilizes a Savitzky-Golay filter in the preprocessing signal stage of the model. After processing the signal, the pipeline LDS begins with the extraction of leak-related features from the AE signal and concludes with the classification of the pipeline's working conditions based on these features. Compared to traditional machine learning techniques, DL methods are more capable of analyzing complex data. DL methods autonomously extract meaningful and discriminating information from complex data for pattern recognition tasks [23; 37]. The most prominent DL methods used for fault diagnosis include deep belief networks, recurrent neural networks, neural auto-encoders, and CNNs. The latter are particularly advantageous due to their ability to reduce the risk of overfitting and because of their low computational complexity, which is achieved through weight sharing, local representative fields, and spatial domain subsampling [38]. The capability of CNNs to extract discriminant information and recognize patterns in one-dimensional (1D) data with low computational complexity while avoiding overfitting makes them especially suitable for pipeline fault diagnosis. Therefore, this study used a 1D CNN for feature extraction from AE signals for pipeline leak detection. As demonstrated in the literature [39], CNN excels in reducing frequency variations, while LSTM recurrent neural networks are adept at temporal modeling. The method proposed herein leverages the strengths of both CNN and LSTM by feeding the output of the 1D-CNN into an LSTM network. Accordingly, an enhanced method for pipeline leak detection using a hybrid CNN-LSTM model is provided. This approach overcomes the limitations of traditional feature extraction methods, as it directly utilizes signal data as an input without the need for conventional feature extraction. The output from the hybrid model is then passed to dense layers for classifying the pipeline condition as either leaking or non-leaking. The key contributions of this paper are outlined as follows:  
1. A hybrid model of pipeline leak detection based on deep learning models is proposed. 2. The proposed model has no requirement of traditional feature extraction and overcomes the limitations of traditional machine learning fault diagnosis techniques. 3. Real-world industrial fluid pipeline AE data are used for validation of the proposed model. 4.