## 4 Conclusions  
The effective prediction of the RUL value for turbofan engines is critical to ensure optimal performance and minimize maintenance costs. This paper proposes a prognostic procedure addressing the feature engineering process and a hybrid network based on 1D-CNN-LSTM. Our approach utilized 1D-CNN architecture to extract spatial information from raw sensor data, while LSTM was used to reveal temporal information from the extracted features. We implemented a range of feature engineering and preprocessing methods. Feature selection, filtering, and normalization were used to improve the data quality and reliability. Also, these methods made it easier for deep learning models to use and interpret the data. In addition, feature engineering approaches, such as dimension reduction and feature creation, not only facilitated the interpretation of existing data but also increased the efficiency of the data and enabled the creation of new features that facilitated the learning of the prediction model. Our proposed prognostic approach demonstrated that the hybrid neural network produced more accurate RUL predictions when combined with practical preprocessing steps. To label the operational life of turbofan engines, we used the PvL target-labeling method, which divides the operational life of the engines into two stages, namely, the stable healthy stage and the linear degradation stage. We leveraged dimensionality reduction for a better representation of the main characteristics and the breakpoints of the sensor measurements. A change point detection method was used to determine the start point of degradation for engine-specific target RUL labeling. Using the maximum RUL specific to each engine presented a challenge due to insufficient degradation information. However, it also brought the solution closer to a realistic scenario. In addition, we constructed a new feature from the existing dataset using the initial sensor measurements to provide more degradation information in the early stage. The proposed methodology provides better prediction results compared with the studies using engine-specific target RUL labels and the actual test dataset. The prognostic procedure proposed for RUL prediction in turbofan engines essentially demonstrated the effectiveness of a feature engineering technique, specifically the feature construction step, and the 1D-CNN-LSTM network model. The proposed hybrid network was tested on multiple hyperparameter combinations using k-fold cross-validation to obtain the best results. The hybrid model was compared with the 1D-CNN and LSTM networks separately, and our findings demonstrate that the CNN feature extraction capability significantly improved the performance of the LSTM network. The results obtained provide evidence for the efficacy of the newly created \"difference\" feature through feature construction. Furthermore, the hyperparameter optimization study revealed a reduced need for deeper and more complex network models in the context of feature engineering processes. The prediction of RUL has been a longstanding and persistently relevant problem, which has been further invigorated by the advancement of artificial intelligence methods. Considering the current trend in various applications related to turbofan engines and other domains associated with the RUL problem, this study was aligned with contemporary requirements. The findings substantiate the impact of data preprocessing on prediction performance and reveal a reduced dependence on complex network structures. For future work, we aim to achieve higher accuracy in the early stage of the deterioration by combining engine-specific RUL labeling, similarity-based methods, and neural networks. We also aim to adopt more robust approaches to determine the change point for PvL labels. Overall, our proposed approach provides promising results and opens avenues for further research in turbofan engines' prognostics and health management. **Author Contributions:** Conceptualization, K.E., T.I. and E.E. ; methodology, K.E., T.I. and E.E.