## 4 Proposed Method  
The proposed method starts by obtaining AE signals with the help of AE sensors and ends with the determination of the pipeline working conditions. Figure 6 shows the overall stepwise description of the proposed method. Each of these steps and their significance are described below. Step 1: AE signals are obtained through AE sensors from the pipeline. Step 2: The AE signals are provided as an input to the Savitzky-Golay filter, which plays an important role in the preprocessing phase of the pipeline data. This filter is used for smoothing the data while preserving the essential features, such as peak heights and positions, which are crucial for accurate analysis and subsequent modeling. The AE signals obtained through the AE sensors can often be noisy owing to various factors, such as environmental variations, sensor malfunctions, or other interferences. The Savitzky-Golay filter helps to reduce this noise by fitting successive subsets of adjacent data points with a low-degree polynomial using a least squares approach. This smoothing process reduces random fluctuations without significantly distorting the signal, making it easier to identify underlying patterns. Unlike other smoothing techniques that may overly simplify the data and damage important features, the Savitzky-Golay filter is designed to maintain the integrity of the original signal's characteristics. It is particularly effective at preserving the sharpness of peaks, which is essential for applications in which the precise detection of peaks, dips, or other significant changes in the signal is important. When the Savitzky-Golay filter is applied, two key variables are used. One is window size \(W\), which is set to 15 in our proposed model; the other is the order of the polynomial denoted by \(O\), which is set to 2 in our proposed model. For the subsequent 1D CNN and LSTM layers to work effectively, it is important that the input data are clean and accurately  
Figure 6: Flow diagram of the proposed method. represent the underlying trends. By smoothing the data, the Savitzky-Golay filter helps ensure that the convolutional filters in the CNN layers and the memory units in the LSTM layers can better detect and learn from the true patterns in the data. This preprocessing step enhances the quality of the features extracted by the model, leading to better performance in classification tasks. By reducing noise and preserving signal characteristics, the filter helps improve the overall accuracy and robustness of the model. This ensures that the model focuses on the meaningful variations in the data rather than being misled by random noise, which could otherwise lead to overfitting or inaccurate predictions. Step 3: The preprocessed signals obtained from the Savitzky-Golay filter are provided as an input to the 1D Hybrid CNN-LSTM model, which is shown in Figure 7. In this model, initially, there are 1D-CNN layers, which are fundamental in the initial stages for their ability to extract local spatial features from the sequential data. The convolutional layers apply filters across the input sequences, effectively capturing patterns and structures within short windows of time. Using multiple filters, the 1D CNN layers can learn to identify various relevant features, such as peaks, trends, and other significant patterns in the sensor signals. This spatial feature extraction process is important because it transforms raw input data into a more abstract and informative representation, which is easier for subsequent layers to process. The inclusion of max pooling layers after the convolutional operations serves to reduce the dimensionality of the data, decreasing the computational burden and helping to highlight the most important features. Max pooling achieves this by down sampling the output of the convolutional layers, retaining only the most prominent features.