## 4 Proposed Method
The key advantages of 1D CNN layers in this hybrid model include their efficiency in local feature extraction, reduced parameter count through parameter sharing, and the ability to perform dimensionality reduction, which collectively enhance the model performance and computational efficiency. These layers prepare the data by extracting and refining features that are important for accurate time series analysis. LSTM layers are introduced after the 1D CNN layers to capture the temporal dependencies inherent in sequential data. Unlike traditional RNNs, LSTMs are designed to remember information over long periods, which is achieved through their unique cell state and gating mechanisms. These mechanisms enable LSTM units to selectively retain or forget information as the data pass through the network, making them particularly effective for time series data where the relationships between distant time steps are significant. The LSTM layers in this model are tasked with learning the temporal dynamics of the sensor data. By processing the sequential features extracted by the CNN layers, the LSTM units can capture patterns over longer durations, such as recurring sequences or trends that  
Figure 7: Schematics of the hybrid CNN-LSTM model. span multiple time steps. This capability is essential for accurately modeling the behavior of sensors, where the signal at any given time point is often influenced by past readings. The advantages of LSTMs include their ability to handle the vanishing gradient problem commonly faced by traditional RNNs, their effectiveness in handling long-term dependencies, and their suitability for sequential data processing. By integrating LSTMs, the model has the ability to understand and predict complex temporal patterns, enhancing its accuracy and robustness in time series classification tasks. Additionally, dropout layers are used to prevent overfitting. By randomly deactivating a fraction of the neurons during training, the dropout forces the network to develop a more robust and generalized understanding of the data rather than relying on specific pathways. Step 4: In the final step of the model, the classifier layers determine the working condition of the pipeline, i.e., whether it is or is not leaking. After the data are processed through the hybrid model, they are transformed into a high-level feature representation. The dense layer maps this representation to the output classes. The dense layer uses a sigmoid activation function, providing a clear and interpretable classification output. The flexibility of the dense layer allows it to be easily adapted to different classification problems by simply adjusting the number of output units and the activation function. The significance of the dense layer is its ability to synthesize the extracted spatial and temporal features, ultimately determining the class of each input sequence. Its integration with the preceding hybrid model layers forms a powerful end-to-end model that is capable of handling complex time series data. Table 2 shows the network architecture of the proposed hybrid model. Table 3 provides details about the different hyperparameter values of the proposed model.