###### Abstract  
In the railway industry, the rail is the basic load-bearing structure of railway tracks. The prediction of the remaining useful life (RUL) for rails is important to avoid unexpected system failures and reduce the cost of maintaining the system. However, the existing detection of rail flaws is difficult, the rail deterioration mechanisms are diverse, and the traditional data-driven methods have insufficient feature extraction. This causes low prediction accuracy. With objectives set in relation to the problems outlined above, a rail RUL prediction approach based on a convolutional bidirectional long- and short-term memory neural network with a residual self-attention (CNNBiLSTM-RSA) mechanism is designed. Firstly, the pre-processed vibration data are taken as the input for the convolutional bi-directional long- and short-term memory neural network (CNNBiLSTM) to extract the forward and backward dependencies and features of the rail data. Secondly, the RSA mechanism is introduced in order to obtain the contributions of the features at different moments during the degradation process of the rail. Finally, an end-to-end RUL prediction implementation based on the convolutional bi-directional long- and short-term memory neural network with the residual self-attention mechanism is established. The experiments were carried out using the full life-cycle data of rails collected at the railway site. The results show that the method achieves a higher accuracy in the RUL prediction of rails. heavy duty; trains rail; deep learning; lifetime prediction +
Footnote †: journal: Medicalinformation. Therefore, the research focus, when considering RUL, is on how to use data-processing and -mining techniques to construct suitable models and extract typical features from rail data. Residual life prediction models are generally classified as physical model methods, statistical model methods, and data-driven methods [4]. The physical modeling approach assesses the health of a system through the construction of mathematical models based on failure mechanisms or first principles of damage [5]. [PERSON] et al. [6] proposed a numerical model based on the simulation of rail profile wear, which, by applying improved models such as Kalker's non-Hertzian contact, simulated the shape of the worn rail profile in general agreement with the field measurements. [PERSON] et al. [7] developed a numerical model of vehicle track based on the multi-body dynamics and the improved Archard wear formula, and a numerical model of vehicle trajectory was developed to characterize the development of high rail-side wear in a heavily trafficked transport track. This approach is highly descriptive as system degradation modeling relies on natural laws. However, the method suffers from problems, such as high cost and complexity of implementation, and is not readily accepted in engineering practice [8]. The statistical modeling method is based on historical data and statistical learning, which builds a prediction model by fitting and analyzing historical data to predict the RUL. [PERSON] et al. [9] used an inverse Gaussian (IG) stochastic process to establish a structural resistance degradation model, which was updated in real time using the Bayesian updating theory and demonstrated the feasibility of the method with numerical examples. Due to the irregularity of the track structure damage, it is very difficult to establish the exact mathematical statistics in practical applications, and the dynamic characteristics of the machine cannot be taken into account. In recent years, neural network methods, like convolutional neural networks (CNNs) [10; 11] and recurrent neural networks (RNNs), have become the most commonly used data-driven-based methods [12]. By extracting features, they reveal potential correlations and causal relationships between the collected data and the health state of the machinery and provide an end-to-end solution for solving the RUL prediction problem [13].