## 1 Introduction  
### Background  
The electromechanical systems in stations are important parts of urban rail transit systems, as they provide services for the safe and orderly passage of passengers through stations. When station electromechanical equipment fails, the station service capacity declines, and serious faults can even induce safety accidents. Especially in the fully automatic operation mode, more equipment and interfaces are present in station electromechanical systems, which places higher requirements for the safe and stable operation of these systems. A platform screen door (PSD) system is critical equipment in modern metro engineering, and it is installed at the edge of the platform to protect the safety of passengers and prevent accidents caused by passengers falling from the platform [1, 2]. A PSD system consists of two parts: the mechanical structure and electrical equipment. The mechanical part includes a door body structure and a door machine system, while the electrical part includes a monitoring system and a power supply system [3]. Platform screen door systems play a significant role in ensuring passengers' safety and the service level of rail transit operations. At present, the maintenance methods for PSD systems mainly include corrective maintenance, preventive maintenance, and predictive maintenance [4]. Corrective maintenance is a maintenance method in which assets are passively repaired after a fault, and maintenance requirements are determined through on-site inspection [5]. Preventive maintenance usually relies on a fixed schedule or mileage interval [6]. However, these two maintenance methods lack consideration of the actual operation of a station's electromechanical equipment, which may cause the system to reduce availability due to imperfect maintenance, thereby reducing its performance [7]. Predictive maintenance is based on characteristics that are predicted or known as a result of repeated analyses and the evaluation of important parameters of project degradation, and state-based maintenance is performed to extend the service life of the system [8; 9]. It has become the main technical way to meet the needs of urban rail transit networks and automatic operation and to improve the level of operation service. Predictive maintenance is a data-driven maintenance strategy that requires a large amount of high-quality data [10]. As PSD systems are an important component of station electromechanical equipment, fault data are only recorded when malfunctions occur. Existing research mainly uses the fault rate to achieve fault prediction for PSD systems. The original fault data only affect the calculation of the fault rate and do not affect the amount of fault rate data. The data volume of the fault rate is related to the time span of the original fault dataset, and it is a type of low-frequency data, making it difficult to meet the data requirements of predictive maintenance. In recent years, the SMOTE algorithm has been widely used for data augmentation; [PERSON] et al. used the SMOTE method based on the Euclidean distance from the center to enhance the data in an access control terminal fault dataset [11]. [PERSON] and [PERSON] used the boundary SMOTE method to enhance data on the faults and residual life of machinery [12]. [PERSON] et al. used the average radius SMOTE method to enhance a gear fault dataset [13]. The SMOTE method finds the nearest neighbors for each minority class sample and randomly interpolates between them to generate new samples, thereby achieving sample class balance before training the classifier [14]. However, the SMOTE method is mainly aimed at solving the problem of class imbalance [15]. The main problem of fault data enhancement of station electromechanical equipment is the generation of time-series data with temporal correlations that conform to the fault mode of platform screen doors, which is difficult to achieve with the SMOTE method.