## 1 Introduction  
The demand for motors has been steadily increasing due to the advancements in industrial automation, necessitating reliable long-term operation. Consequently, the significance of motor operation monitoring systems has become paramount. Historically, motor fault diagnosis has been segmented into three primary phases. Initially, physical signals from the motor, such as voltage, current, temperature, and vibration, are captured by sensors and stored in a database. Subsequently, features are extracted from these captured signals using various discrete signal processing techniques or time-frequency analyses, including empirical mode decomposition (EMD), variational mode decomposition (VMD), Hilbert transform, and wavelet packet transform (WPT). The final phase involves performing motor fault diagnosis based on these extracted features [1, 2, 3]. However, a fundamental drawback of these traditional fault diagnosis methods is the requirement to consider denoising, filtering, and feature extraction during design. This requirement significantly increases the complexity of the diagnostic process. Furthermore, these methods do not support the automatic diagnosis of motor faults, presenting a substantial limitation in practical applications [4]. In recent years, artificial intelligence (AI) has been extensively applied in motor fault diagnosis technologies, with numerous model architectures and signal processing techniques being successively proposed, all achieving high diagnostic performance. [PERSON] et al. utilized the deep belief network to diagnose induction motor faults [5]. [PERSON] and [PERSON] et al. developed a motor-bearing fault diagnosis method that combines deep learning with information fusion [6]. Further, [PERSON] et al. transformed one-dimensional vibration signals into two-dimensional time-frequency images, which were subsequently processed using a deep convolutional neural network (CNN) to detect faults in induction motors [7]. [PERSON] et al. introduced a one-dimensional CNN model that includes multiple convolutional feature extraction modules to identify healthy, demagnetization fault, and bearing fault states [8]. Additionally, [PERSON] and [PERSON] employed current and vibration signals as inputs for the artificial neural network to detect faults in brushless DC (BLDC) motors [9]. Although these methods can achieve effective motor fault diagnosis, they require a substantial amount of training data and a balanced dataset to ensure satisfactory diagnostic outcomes [10]. However, in real-world industrial settings, motors predominantly operate in healthy states, and the data acquisition systems primarily collect data from healthy motors [11]. Consequently, motor fault data under varying operational conditions are exceedingly scarce, leading to severe imbalances in the training data [12]. The imbalance of training data causes overfitting during the model training process, and the accuracy of the minority class with few data is lower [13]. As a result, the trained deep learning models often lack generalization and practical utility. Moreover, labeling fault data under different working conditions demands significant human resources and incurs high economic costs [14]. These challenges substantially diminish the efficacy of intelligent motor fault diagnosis systems. As mentioned earlier, in the induction motor fault diagnosis field, data are typically collected from normal operations and greatly outnumber the fault data, leading to a highly imbalanced dataset. This imbalance in training data makes it difficult for classifiers to identify different fault categories accurately [15]. The higher the imbalance ratio, the more significant the decline in the diagnostic effectiveness of the classifier, especially in terms of accuracy in identifying fault categories [16]. Therefore, addressing data imbalance is crucial for improving fault diagnosis accuracy. Common methods such as over-sampling and down-sampling are employed to manage data imbalance effectively [17].