###### Abstract
Therefore, these data-driven approaches have gradually become mainstream in recent years. The long short-term memory network (long short-term memory, LSTM) [14] and its variants have all been successfully adapted to the field of RUL prediction in succession. [PERSON] et al. [15] introduced a deep neural network based on a convolutional long short-term memory (CLSTM) network, which contains the time-frequency information as well as the temporal information of the signal. This network retains the advantages of LSTM and incorporates time-frequency features to accurately predict RUL. However, the LSTM only predicts future data by capturing past data. Bi-directional long short-term memory (BiLSTM) simultaneously acquires future and past information, demonstrating better qualities in RUL prediction [16]. [PERSON] et al. [17] used a BiLSTM neural network to learn intrinsic features in both directions and improved the fault recognition accuracy. [PERSON] et al. [18] proposed a multi-branch improved convolutional network (MBCNN)-BiLSTM model for predicting bearing RUL. MBCNN achieves spatial feature extraction of orientation input data, and then BiLSTM further mines the temporal features of the data, thus improving the prediction accuracy of bearing RUL. [PERSON] et al. [19] proposed a complete ensemble empirical mode decomposition (CEEMD)-CNN-LSTM model, which was experimentally verified to have higher prediction accuracy than single CNN and LSTM. The above study shows that fully extracting data features can improve the prediction performance and achieve better prediction accuracy. Although CNNs and LSTM can extract data features well, they are not fully suitable for rail vibration signals, because CNN is deficient in temporal feature extraction and LSTM is deficient in local feature extraction. In contrast, combining the two can make better use of the advantages of both, thus improving the ability of feature extraction. Due to the special characteristics of the existing rail inspection, the full life-cycle data of rail injury and damage have the characteristics of low frequency and long time span, and at the same time, it is difficult and costly to explore the failure mechanism of rails involving complex wheel-rail relationship problems. In addition, a series of problems, such as gradient explosion and disappearance, and large memory occupation, have not been properly solved during the process of model training. Meanwhile, if the prediction accuracy is not enough, it may cause significant economic damage and casualties. This poses a challenge to deep learning-based RUL prediction for railway tracks. In recent years, attention has arguably become one of the most important concepts in the field of deep learning. With the development of deep neural networks, the attention mechanism has been widely used in various application areas [20]. It has been shown that the attention mechanism has better access to the extracted feature information [21]. [PERSON] et al. [22] proposed an attention mechanism to solve problems such as gradient vanishing and gradient explosion. The method calculates different weights given to different features of the model. [PERSON] et al. [23] proposed an end-to-end RUL prediction method based on feature-attention, which applies the proposed feature-attention mechanism directly to the input data so as to dynamically assign greater weights to more important features during training, thereby improving the prediction performance. The residual connection can enhance the gradient propagation, thus solving the problem of the increase in network depth that can easily cause gradient disappearance and gradient explosion [24]. [PERSON] et al. [25] proposed a CNN-LSTM-Skip model to estimate the State of Health (SOH) of lithium-ion batteries. Jump connections were added to the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model in order to solve the problem of neural network degradation caused by multilayer LSTM. [PERSON] et al.