# Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
## 5 Discussion  
The proposed CNN-LSTM hybrid model, applied to AE data from industrial fluid pipelines, demonstrated outstanding performance, achieving precision, accuracy, F1 score, and recall values of 99.71%, 99.69%, 99.82%, and 99.75%, respectively, as presented in Table 5. These results highlight the model's superiority over reference models, including CWT-CNN, FFT-CNN, and STFT-CNN, in terms of classification accuracy. The model's enhanced performance stems from its ability to integrate spatial and temporal features effectively. The CNN component extracts spatial features from enhanced CWT scalograms, capturing intricate energy variations in the AE signals. Meanwhile, the LSTM component excels in modeling temporal dependencies, identifying meaningful sequential patterns within the data. Additionally, the inclusion of a genetic algorithm (GA) refines the extracted features, ensuring that only the most relevant and discriminative features are selected for classification. This hybrid architecture enables a seamless combination of spatial and temporal feature extraction, significantly enhancing the model's capacity to differentiate fault conditions accurately. The integration of these advanced techniques contributes to the model's robust performance across various experimental conditions and performance metrics, establishing it as a highly reliable solution for fault detection and classification in industrial pipeline systems. [PERSON] et al. [39] collected acoustic signals from a gas pipeline system with small, synthetic leaks to apply deep learning techniques for leak detection. Their approach involved introducing controlled artificial leaks, which exposed the system to diverse acoustic signatures under predefined conditions. This deliberately created input was important for training the model to recognize patterns associated with small leaks, simulating real-world conditions effectively. The system utilized these inputs to iteratively optimize its performance, refining its robustness and adaptability by addressing edge cases through expert feedback. By transforming the acoustic signals into the frequency domain and applying a 1D CNN model, the methodology was able to develop discriminative features for small-leak detection. However, noise in the acoustic signals introduced challenges that impacted performance, resulting in an accuracy of 85.18%. Despite these limitations, the approach demonstrated its effectiveness and was selected for comparison due to its compatibility with our experimental setup. The performance metrics in Table 5 reflect the application of this reference technique to our dataset. [PERSON] et al. [40] employed a DL approach for leak detection by collecting data using a hydrophone from a leaking plastic tank. Their study systematically compared multiple signal preprocessing techniques across the frequency domain, time domain, and time-frequency domain, with each preprocessed signal subsequently analyzed using a convolutional neural network (CNN) for feature extraction and classification. The study revealed that the FFT-CNN approach outperformed other preprocessing methods in effectively detecting leaks. To ensure a fair and consistent comparison, the same methodology was applied to our pipeline dataset, where signals underwent similar preprocessing across the time, frequency, and time-frequency domains before being processed by a CNN model. The results obtained from our dataset were carefully recorded and analysed, allowing for a direct comparison with ABC's findings to evaluate the relative performance of each method in the context of pipeline fault detection. The proposed model was compared with a TFD method, specifically the STFT-CNN. In this approach, AE signals from the TD are transformed into the TFD using the STFT. The resulting representations are fed into a CNN, which is trained to extract features indicating leaks. This method utilizes the CNN's pattern recognition capabilities combined with STFT for feature extraction.