###### Abstract
To address the problem of prognosis algorithm selection, the state variables of components of brake systems form a time series during its performance degradation process, and the time series prognosis algorithms commonly used include traditional probabilistic statistical analysis-based algorithms, and machine learning-based algorithms [21]. Of these, prognosis models based on traditional probabilistic statistical analysis methods mainly include autoregressive models, autoregressive sliding average models, and autoregressive integrated sliding average models [22; 23], which are simple and easy to implement. However, they are generally suitable for proposing stable time series and are not suitable for brake systems with variable operating conditions, continuous non-periodicity, large dynamic range of input and output time series, and strong stochasticity. With the continuous development of machine learning methods, artificial neural networks (ANNs) and support vector regression (SVR) have been widely used in the field of nonlinear time series prediction. [PERSON] [24] used radial basis function neural networks to predict the vibration trends of aircraft engines. [PERSON] [25] proposed a real-time health state prediction method for batteries of electric vehicle by using neural networks. [PERSON] [26] proposed a gyroscopic drift time series prediction method based on phase space reconstruction and SVR. [PERSON] [27] proposed a multivariate support vector machine (SVM)-based method for predicting the remaining life of rolling bearings. [PERSON] [28] used SVM for machine performance degradation assessment and remaining useful life prediction. Although ANN and SVR have a high prediction accuracy for non-linear time series, the algorithms themselves do not adequately consider the temporal memory properties (continuity and correlation) between the data points in the state variable time series during the performance degradation process. In fact, the performance degradation process of components of brake systems is generally slow, varying, and closely related to time. For example, relay valves of brake systems are dominated by wear-and-tear failures and characterized by a gradual performance degradation process. In order to make full use of time series data for prediction, [PERSON] [29] proposed a recurrent neural network (RNN) to estimate the remaining useful life. The input information of each layer of the RNN depends on the output information of the previous layer, introducing the concept of time series into the design of the network structure and considering the correlation between data points in the time series. However, RNN suffersfrom a lack of long-term memory capacity and tends to suffer from gradient disappearance when processing long-term data [30]. To address this problem, [PERSON] [31] proposed a long short-term memory (LSTM) network, which not only takes into account the correlation between data points in a time series, but also effectively improves the long-term dependency of RNN, making it possible to predict performance degradation trends considering historical and real-time operational data. [PERSON] [32] proposed a method for predicting the remaining life of bearings based on convolutional long and short-term memory neural networks. [PERSON] [33] proposed a passenger flow prediction method based on LSTM networks by considering both historical and real-time data. [PERSON] [34] used LSTM networks to estimate the remaining service life of engineering systems and demonstrated the predictive performance of the method by using a case study on an aircraft turbofan engine. However, due to the problem that the signals between the components of the brake system are coupled, the magnitude of the signal is affected by variable operating conditions, and the performance degradation cycle is long, the performance degradation trend is not completely clear.