###### Abstract
Furthermore, a double-channel hybrid deep neural network containing CNN and BiLSTM layers was applied for better prediction performance [18]. A lower-dimensional projection of the sensor measurements obtained from a CNN-based autoencoder method was used as input for CNN-LSTM serial layers in [31]. The features from a one-dimensional fully convolutional neural network and LSTM network were fused and then fed into the following CNN network to improve the effectiveness of the prediction model [1]. A group of studies used an attention mechanism, which converts the original input into weighted input to represent the features better. For instance, an attention mechanism was used in the bidirectional gated recurrent units (BiGRUs)-CNN hybrid neural network in [32]. [PERSON] et al. [33] used an attention-based temporal convolutional network (TCN) and the BiGRUs for RUL prediction. A random forest (RF) algorithm was used to rate the importance of the features and implement variational mode decomposition on these features. Another group of studies focused on constructing new features to contribute to the learning of degradation processes. By taking the first-order differences for the time series of sensor measurements, new features were created and added to the dataset [19; 34]. Correlation-based degradation features were used in [35], while mean and trend coefficients were used in [36]. A complete ensemble empirical mode decomposition method was used to create sensor measurement trend features [37]. [PERSON] and [PERSON] [38] transformed the time-based cycle feature into a physical health time step to provide meaningful features. The studies presented above used a PwL function for target RUL labeling. The constant and maximum part of the piecewise RUL, mostly named \(\text{RUL}_{\text{max}}\), is common for all the engines in the dataset. This approach is helpful for generalization but does not accurately reflect the situation of turbofan engines in the real world. The degradation process of each engine differs from the other. The studies on engine-specific PwL target labeling are less frequent than studies using a common \(\text{RUL}_{\text{max}}\) for all engines. The main reason is the lack of sufficient data to differentiate engines from each other while predicting the RUL in the healthy state of the engine. Also, incomplete sensor data in the test dataset hinders precise change point determination. Most studies using the engine-specific \(\text{RUL}_{\text{max}}\) focused only on the training dataset. In general, health state (HS)-division-based specific RUL labeling was studied in a dual-task manner for both RUL prediction and HS evaluation simultaneously [39; 40]. A dual-LSTM framework was used for change point detection in RUL labeling and HI-based RUL prediction [41]. The RUL prediction in the healthy state is difficult due to the lack of information about the degradation process, and thus, the RUL prediction was made using the sensor measurements after the change point [42; 43]. The challenge of predicting the RUL is not confined solely to turbofan engines but spans across diverse domains, encompassing applications in areas such as Li-ion batteries and bearings, where RUL prediction plays a prominent role. In [44], an LSTM and gradient boosting machine (GBM) were utilized to analyze Li-ion batteries, combined with explainable artificial intelligence techniques for feature selection. The study in [45] introduces a novel approach for improving the accuracy of bearings' RUL prediction, combining a multi-branch convolutional network (MBCNN) with global attention and a BiLSTM network, utilizing both spatial and timing features from vibration signals, which were ultimately tested on a public bearing degradation dataset. Also, the significance of RUL labeling on RUL prediction is demonstrated in the study by [46] with load calculations, which assessed bearings.