## 1 Introduction  
Energy-savings, speed, and comfort are favored by passengers of the \"Fuxing\" Chinese Standard Electric Multiple Units (EMU). While people enjoy the convenience of standard EMUs, their safety is widely emphasized. As one of its nine key technologies, the EMU's brake system directly affects its operational safety and efficiency. The relay valve, EP valve, and empty and load valve are the key pneumatic units of the brake system, which determine the reliability and availability of service and emergency braking [1; 2; 3].  
However, during long-term operation, leakage faults may occur due to the wear of rubber seals, spring fatigue, and mechanical jams caused by grease failure. These faults may degrade the performance of the pneumatic unit and even cause functional failures, such as the brakes not releasing or providing insufficient braking power, all of which can affect operational safety and efficiency [4; 5; 6; 7; 8; 9]. Although the existing brake system has a diagnostic function, this function offers a system-level fault diagnosis and does not have a component-level fault warning function. When the leakage of a pneumatic unit occurs, the most noticeable result is a change in pressure. Consequently, the pressure state can be monitored to identify and diagnose latent leakage problems. As a statistical test method, sequential probability ratio test (SPRT) is commonly used for on-site monitoring and isolation of abnormalities. Compared to conventional threshold detection methods, SPRT requires the smallest average number of samples for the fault detection of signals, which means that a developmental fault signal can be indicated earlier with the same accuracy. Meanwhile, considering the particularity of the braking system--that is, the braking force required by the vehicle under a different vehicle load and braking level is different--it is difficult for the traditional SPRT to prepare a complete training set. In this paper, the Kalman predictor is used to preprocess the pressure signal, and the innovation sequence obtained by Kalman filtering is used as the detection signal of SPRT.  
Due to the distinct impacts of different leakage faults, this paper extracts fault characteristic parameters to realize the fault diagnoses of relay valve leakages, to provide guidance for maintenance decisions and save maintenance costs. At present, the most efficient diagnostic methods for hydraulic (pneumatic) system faults mainly include the fault tree [10], the Back Propagation (BP) neural network [11; 12; 13], and the expert system [14]. Among them, the fault tree has great limitations in online diagnosis. The BP neural network method has a slow convergence speed, which lengthens the training time, and makes it easy to get the local minimum; conversely, the amount of data required by the expert system is relatively large, and the diagnostic results can be inaccurate. The support vector machine (SVM) [15; 16] algorithm has special advantages in small sample, high dimensional, and nonlinear problems. From a theoretical point of view, the SVM algorithm obtains the global optimal solution, which makes up for the fact that the BP neural network method can easily fall into the local extremum. At the same time, the SVM can obtain higher diagnostic accuracy under small sample training and fixes the expert system method's low diagnostic rate in the case of incomplete samples.  
In this paper, the key valve shared by the service braking and emergency braking of the brake system--the relay valve--is taken as an example for research. The multi-source information fusion method of the Kalman filter, SPRT, and SVM is proposed to be applied to the standard EMU for identifying and diagnosing latent leakage faults in the critical pneumatic units of the brake system. Considering the limited number of relay valve faults and the research of the leakage mechanism of the relay valve, the leakage fault simulation test is carried out to obtain the fault sample data and to verify the feasibility of the method.