###### Abstract  
Accurate remaining useful life (RUL) prediction is one of the most challenging problems in the prognostics of turbofan engines. Recently, RUL prediction methods for turbofan engines mainly involve data-driven models. Preprocessing the sensor data is essential for the performance of the prognostic models. Most studies on turbofan engines use piecewise linear (PwL) labeling, which starts with a constant initial RUL value in normal/healthy operating time. In this study, we designed a prognostic procedure that includes difference-based feature construction, change-point-detection-based PwL labeling, and a 1D-CNN-LSTM (one-dimensional-convolutional neural network-long short-term memory) hybrid neural network model for RUL prediction. The procedure was evaluated on the subset FD001 of the C-MAPSS dataset. The proposed procedure was compared with machine learning and deep learning models with and without the new difference feature. Also, the results were compared with the studies that used similar labeling approaches. Our analysis of the numerical results underscores the clear superiority of the proposed 1D-CNN-LSTM model with the difference feature in RUL prediction, with a score of 437.2 and an RMSE value of 16.1. This result illustrates the superior predictive capability of the 1D-CNN-LSTM model, which outperformed traditional machine learning methods and one of the earliest deep learning methods. These findings emphasize the superior predictive capability of the 1D-CNN-LSTM model and underline the potential of the feature engineering process for more accurate and robust RUL prediction in the context of turbofan engine prognostics. remaining useful life; prognostics and health management; feature engineering; turbofan engines; change point detection; convolutional neural network; long short-term memory network Article  
[PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Remaining Useful Life Estimation of Turbofan Engines with Deep Learning Using Change-Point Detection Based Labeling and Feature Engineering  
1311893  
when a component or a system no longer maintains its healthy state and useful life [6]. RUL prediction allows for reducing costs, scheduling appropriate maintenance and replacement plans, and preventing occupational accidents. PHM plays a critical role in industries like aviation, where reliability is paramount due to the potentially devastating impact of accidents in terms of human life and economic losses. In this study, we focused on turbofan engines because a turbofan engine is the most critical component of an aircraft. It is one of the most complex components that require high reliability and quality [7]. RUL prediction is essential for developing maintenance strategies to reduce the cost and reliability aspects of turbofan engines [8]. Existing RUL prediction methods can generally be categorized into model-based, data-driven, and hybrid approaches [9; 10]. Model-based approaches are helpful methods that accurately describe system degradation using mathematical models [11]. However, building mathematical models in complex systems can be difficult and costly, requiring expertise and domain knowledge about physical systems. Data-driven approaches can reveal the characteristics of system degradation by converting historical sensor data into useful information. With advanced sensor and computer technologies, data-driven approaches have been widely used to learn about complex system degradation with less expertise in industry and academia. Hybrid approaches are methods in which the two aforementioned approaches are used together to take advantage of model-based and data-driven approaches. However, hybrid approaches also have limitations due to both methods, making the use of hybrid approaches challenging. In this study, data-driven approaches were considered in the RUL prediction of turbofan engines. Data-driven approaches are based on statistical techniques and artificial intelligence [12].