## 4 TCN-Autoencoder  
The proposed model presented in the current study introduces a new approach for accurate anomaly detection by taking advantage of the synergistic capabilities of Temporal Convolutional Network (TCN) and Multi-Head Attention (MHA) mechanisms. This model has a major challenge that requires models that are able to recognize intricate temporal patterns and subtle deviations that act as anomaly indicators. This architecture is designed to capture a wide range of temporal dependencies in time-series data and increase model sensitivity to anomalies. Current methods for fault diagnosis in gas turbines, which mainly focus on traditional machine-learning techniques and rule-based systems, face several important challenges:  
1. Limited adaptability: many existing models, including rule-based systems and early machine learning, try to adapt to the variable operating modes of modern gas turbines; this limits their effectiveness in dynamic environments where operational parameters change frequently. 2. Scalability issues: traditional methods often do not scale well with the increasing volume of data generated by modern sensor technologies installed in turbines, leading to performance degradation as the volume of data increases. 3. Insensitivity to subtle anomalies: most legacy systems are designed to detect large-scale deviations, which makes them less efficient at detecting subtle anomalies that can precede major faults. 4. High false alarm rate: due to their generalized nature, these systems often have a high rate of false alarms, which can lead to unnecessary shutdown and maintenance activities, and increase operating costs. Meanwhile, our proposed model introduces several innovative features that address the above-mentioned challenges:  
1. TCN architecture: Unlike traditional RNNs, TCNs offer efficient processing through parallelization and can capture long-range dependencies in time-series data. This makes our model highly effective in real-time fault detection scenarios. 2. MHA mechanism: This feature allows the model to focus on different parts of the data simultaneously, enhancing its ability to detect subtle anomalies that are indicative of potential faults. This directly addresses the issue of insensitivity in many traditional models. 3. Anomaly detection through reconstruction: The autoencoder aspect of the model focuses on reconstructing the input data and identifying deviations from the norm. This approach reduces false positives by distinguishing between normal fluctuations and actual anomalies. 4. Scalability and adaptability: Our model is designed to be scalable by incorporating additional sensor data and adapting to new unforeseen operational conditions without requiring extensive retraining. By incorporating these features, our TCN-Autoencoder model not only outperforms traditional fault detection systems in terms of accuracy and efficiency but also significantly enhances predictive maintenance capabilities. This novel approach is expected to reduce maintenance costs and improve the reliability and safety of gas turbine operations. Figure 5 presents the structure of the advanced multi-scale TCN-Autoencoder. The core of the model is built on TCN blocks, which are suitable for sequential data due to their convolutional nature. Unlike traditional convolutional networks, TCNs offer the advantage of larger receptive fields through expanded convolutions, making them ideal for time-series data where long-term dependencies are critical. In this model, several TCN branches were developed in parallel, each with a unique dilation rate (dilation_rate parameter with values of 1, 2, and 4) which helps the network capture different levels of detail in time-related features. This multi-branch setup ensures that the model can detect anomalies on different time scales, from instantaneous to longer deviations. Following the TCN layers in each branch, it applies layer normalization to normalize activations and stabilize the learning process.