###### Abstract
When considering the studies conducted not only within the realm of RUL prediction for turbofan engines but also in various other applications, it becomes evident that there is an apparent necessity for comprehensive and pioneering research in the domains of target labeling approaches, feature engineering processes, and the development of effective network models. This study primarily investigated data-driven approaches, specifically deep learning techniques, for turbofan engine RUL prediction. Various deep learning methods, including long short-term memory (LSTM) and convolutional neural networks (CNNs), were examined, and this paper proposes a novel approach that combines change-point-detection-based target labeling and feature construction for RUL prediction. This research aimed to improve the accuracy of RUL prediction in different stages of the engine's life and used engine-specific RUL labels for a more realistic representation of engine behavior. This paper presents a comprehensive experimental study using turbofan data and the results were compared with existing methods to demonstrate the proposed method's effectiveness. The main contributions of this paper are as follows:  
1. To the best of our knowledge, this study was the first to combine change-point-detection-based target labeling and feature construction in RUL prediction. 2. In line with the current trend in RUL prediction, we embraced a flexible target-labeling approach, employed innovative feature engineering strategies, and introduced an efficient hybrid network to enhance prediction accuracy while minimizing computational complexity, as validated through comparisons. 3. Most studies used PwL target labels for RUL prediction with a constant \(\text{RUL}_{\text{max}}\) value like 120, 125, and 130. However, this study used an engine-specific PwL target label for each engine. 4. Previous studies used the original training dataset for both training and testing by splitting it into two disjoint sets. Different from them, this study focused on predicting the RUL using the original test dataset to ensure a fair evaluation. 5. We fitted continuous PwL functions on the one-dimensional data that was fused using an autoencoder-based feature extraction method for engine-specific target RUL labeling. We use the Python library pwlf, in which the unknown breakpoints are determined with the differential evolution-based optimization algorithm. An increasing number of line segments were used until there was no significant improvement in finding the first change point. The earliest first change point was selected for piecewise target RUL labeling. 6. A handcrafted feature was constructed, which includes the difference between the sensor measurements in every cycle and the first sensor measurement, and an added feature to the dataset. Thus, RUL prediction was improved in the middle- and early-life stages. The remainder of this paper starts with the theoretical background of the deep learning methods and feature engineering approaches in Section 2, along with the proposed architecture of the neural networks used in this study. The experimental study was performed using the FD001 sub-dataset of the C-MAPSS dataset. The results are compared with the studies using similar labeling methods in Section 3. Finally, Section 4 concludes the paper and proposes future work.