## 1 Introduction  
High-voltage circuit breakers (HVCBs) are a vital part of power supplies because they protect and control power systems. The reliability of their operation directly affects the power system itself. Once a HVCB fault occurs, it may cause significant losses to industrial production and the normal life of urban residents [1, 2]. When a circuit breaker is in the process of opening and closing, its components will produce strong impacts, which will easily cause various faults such as jamming of the iron core and fatigue of the closing and opening springs. According to a 2012 international report on high-voltage circuit breakers, mechanical failures accounted for the largest proportion of circuit breaker failures [3]. To ensure normal power system operation, strengthening the detection of mechanical faults in HVCBs is essential, and one of the important steps is the detection of vibration signals [4]. Considerable information about the mechanical state is included in the vibration signals resulting from mechanical friction and vibration during the opening and closing of HVCBs, so detecting the mechanical condition of circuit breakers using nonintrusive vibration signals has been extensively researched [5, 6]. Mechanical vibration signals are used to diagnose HVCB mechanical failure in this study. The analysis of the equipment's state using artificial intelligence technology and massive data is significant for comprehensively understanding equipment operation. [PERSON] et al. utilized wavelet coherence analysis and deep learning to accurately identify faults in centrifugal pumps [7]. [PERSON] et al. employed deep learning in combination with enhanced short-time Fourier transform spectrograms and continuous wavelet transform scalograms to precisely detect and classify pipeline leaks [8]. Data-driven methods have exhibited excellent performance in the diagnosis of mechanical faults in HVCBs in recent years. These methods stand out by exploring the correlation of defect samples and defect types [9]. With advances in intelligent technology, some scientific research results have been obtained in the state detection technology of high-voltage switchgear operating mechanisms and transmission. [PERSON] and [PERSON] [10] applied a neural network and expert system for fault diagnosis in HVCBs. [PERSON] [11] adopted a wavelet packet and support vector machine (SVM) method to accurately diagnose circuit breaker faults, but its optimization algorithm training is complicated and its ability to generalize is weak. [PERSON] et al. [12] used an attention mechanism capsule convolutional neural network in the domain of HVCB fault diagnosis and reached notable achievements. [PERSON] et al. [13] presented a deep belief network and a strategy of transfer learning for the diagnosis of mechanical faults in HVCBs. However, the currently used deep learning methods are applied based on the premise of a great many samples, and the effect will decrease when sample numbers are small. Because of the prolonged closed state of most circuit breakers during actual operation, the occurrence of mechanical failures is relatively rare. As a result, it becomes challenging to gather a sufficient number of samples through measuring instruments alone. During field operations, the failure probability of HVCBs is remarkably low, resulting in a scarcity of actual failure samples. Furthermore, various types of mechanical failures in HVCBs occur at different probabilities, with some failures being less likely, resulting in a scarcity of samples for certain types and a significant imbalance in the training data. This scarcity or imbalance of training samples can severely limit the capability of deep learning feature extraction. Therefore, fault diagnosis of HVCBs under real conditions poses a challenge as it becomes a small-sample problem.