## 1 Introduction  
With the rapid development of information technology and intelligent technology, the massive monitoring data of railway systems provides strong support for the intelligent operation and maintenance of railway equipment and health management. Among them, the condition monitoring and health management of a plunger pump, a key component of a railway switch machine, is of great significance. However, in the actual complex industrial process, the frequency of failure is very low, and normal data are greater in number than fault data. Therefore, the industry's big data often present types of unbalanced characteristics. It is a hot topic to classify non-equilibrium data efficiently and precisely. At present, there are two kinds of classification methods for unbalanced data: the first method changes the data distribution to achieve the data level of data balance between classes; the second approach is an algorithm-level approach that focuses on a small number of data samples. In terms of data, scholars mainly use resampling techniques to adjust original samples to reduce the impact of dataset imbalance. [PERSON], [PERSON] et al. [1] proposed a new oversampling method, the weighted synthetic Minority oversampling technique (WSMOTE). This method generates new synthetic data by randomly selecting some samples of a few classes, and assigns different weights to those data according to the importance of the few classes, which improves the importance of the samples in the classifier. [PERSON] et al. [2] integrated the clustering idea into the undersampling method and dynamically adjusted the undersampling proportion according to the sample distribution and model performance to balance the class distribution more accurately and improve the model performance. [PERSON] et al. [3] put forward the Mean Radius-SMOTE oversampling method to solve the problem of data unbalance in mechanical fault diagnosis. This method is used to calculate the neighborhood radius based on the average distance between the sample point and its nearest neighbors of the same class. [PERSON] et al. [4] proposed an improved SMOTE method (i.e., MSMOTE) for processing datasets with imbalanced classifications and applied this to multi-fault diagnosis. [PERSON] et al. [5] combined the SMOTE algorithm with an artificial neural network to solve the problem of sample imbalance and classification in power transformer fault diagnosis, which improved the accuracy and reliability of the model. [PERSON] et al. [6] proposed a hybrid classifier architecture which first uses density undersampling to obtain balanced subsets of multiple categories, and then uses a cost-effective classification method to deal with information incompleteness. [PERSON] et al. [7] proposed a SMOTEOB method that enables the dataset to be rebalanced in a continuous data stream and the classifier to be trained and updated online, thus making the model more adaptable and robust. Aimed at the problem that traditional fault diagnosis methods require a lot of manual experience and that feature extraction is incomplete, [PERSON] et al. [8] discussed the current status and applications of SMOTE to commemorate its 15 th anniversary, and identified the next set of challenges for SMOTE in solving big data problems. [PERSON] et al. [9] used sampling methods to solve regression tasks where the target variable is continuous. The SMOTER method proposed can be used in conjunction with any existing regression algorithm, making it a universal tool for solving rare extremum problems in predicting continuous target variables. [PERSON] et al. [10] studied the properties of SMOTE from both theoretical and empirical perspectives, using simulated and real high-dimensional data to explain the class prediction that affects high-dimensional data. [PERSON] et al. [11] described a method for constructing classifiers from imbalanced datasets, which combines the oversampling of minority classes with the undersampling of majority classes to achieve better classifier performance. [PERSON] et al.