## 1 Introduction
However, GANs face challenges like low training accuracy and high computational demands. Data augmentation techniques are still widely used for balancing datasets, with the synthetic minority oversampling technique (SMOTE) and its variants being particularly common [18]. [PERSON] et al. [19] applied SMOTE to synthesize motor fault samples, enhancing classifier performance. Compared to a GAN, SMOTE requires fewer computational resources and can generate a large number of fault samples, meeting the needs of intelligent diagnostic models [20]. SMOTE has been improved in subsequent algorithms, which have reduced the generation of ineffective samples, as shown in methods like LR-SMOTE and Borderline-SMOTE [21; 22]. However, in areas where minority and majority class samples overlap, SMOTE may unintentionally generate new samples within the majority class, reducing classification performance. To address this issue, [PERSON] et al. introduced the adaptive synthetic (ADASYN) algorithm in 2008. The core concept of ADASYN is to assign different weights to minority class samples and generate new samples based on these weights, effectively mitigating class imbalance [23]. Despite its advantages, ADASYN may still cause ambiguous decision boundaries, especially when handling multiple minority classes with varying sample sizes, increasing the risk of misclassification [24; 25]. In 2015, [PERSON] et al. proposed the Kernel ADASYN method, enhancing the ADASYN approach by incorporating kernel density estimation. This method calculates the probability density of minority class samples and assigns weights according to sample importance [26]. By improving the quality of minority class samples near decision boundaries, Kernel ADASYN enhances the accuracy of downstream classifiers [27]. However, current research on fault diagnosis of rotating machinery mainly focuses on the imbalance between normal and faulty samples, often neglecting the uneven distribution of samples across different fault types. For example, [PERSON] et al. found that Dataset C contains 1500 normal samples, but each of the three distinct fault types is represented by only 300 samples [28]. A further challenge of using deep learning for intelligent fault diagnosis is the high computational resource consumption of the models, which poses significant disadvantages for real-time monitoring applications. To improve real-time monitoring and applications in movable machinery, deep learning-based fault diagnosis models must simplify complexity, reduce parameters, and progress towards lightweight development [29]. Since 2016, several lightweight network architectures have been proposed. SqueezeNet, for example, aims to drastically reduce the number of model parameters while maintaining accuracy comparable to larger, more complex models [30]. ShuffleNet, designed for mobile and embedded devices, increases network efficiency through channel shuffling and grouped convolutions [31]. MobileNet, introduced by [PERSON], is another representative lightweight convolutional neural network that significantly reduces model size while preserving accuracy, comparable to traditional networks like CNN and visual geometry group (VGG) networks [32]. In the field of fault diagnosis, MobileNet has produced notable research results. [PERSON] et al. developed a fault diagnosis model based on MobileNet, implementing an end-to-end intelligent fault classification and diagnosis application [33]. Similarly, [PERSON] et al. [34] proposed a low-complexity MobileNet neural network for classifying different bearing fault vibration signals, achieving over 99% classification accuracy even under various interference conditions. However, lightweight convolutional networks, such as MobileNet, are mainly used for image processing, and studies on their application in fault diagnosis of one-dimensional signals in rotating machinery remain relatively scarce. Existing deep learning models designed to address data imbalance in rotating machinery fault diagnosis face three main challenges. 1.