## 1 Introduction  
As one of the most important parts of rotating machinery, rolling bearings play a decisive role in the smooth operation of equipment [1]. Due to the harsh working environment of most rolling bearings, faults often occur; if they are not detected and troubleshooted in time, not only will there be economic losses, but they may also be life-threatening [2]. Therefore, the use of accurate fault diagnosis methods to monitor the operational status of bearings is a hot topic in current research [3]. As technology and computer arithmetic develop, data-driven DL methodologies are becoming favoured by many scholars, and such methods are able to automatically extract fault features without too much human involvement [4; 5; 6; 7]. [PERSON] et al. proposed a CNN-LSTM based model for bearing fault diagnosis in complex operating environments, and the results show that the model has better load generalisation capability and noise immunity [8]. [PERSON] et al. proposed the RQA-Bayes-SVM for the healthy diagnosis of bearings; experiments showed that RQA-Bayes-SVM has better performance in fault mode diagnosis and fault degree differentiation [9]. [PERSON] et al. proposed the DenseNet-BLSTM for the problem of extracting features effectively using traditional fault diagnosis methods rolling bearings; experiments show that the DenseNet-BLSTM has good fault diagnosis capability [10]. The data-driven deep learning methods mentioned above are usually constructed with datasets that are set to be class-balanced. However, when dealing with unbalanced datasets, these models usually focus on the majority category and may ignore the minority category samples, resulting in low diagnostic accuracy of the minority fault samples [11; 12]. Current research efforts to solve the unbalanced fault classification problem have focused on the following two areas:  
(1) Data level: resampling techniques are mainly used to convert an unbalanced dataset into balanced dataset to enhance the diagnostic capability of the model. [PERSON] et al. used a generative adversarial network (GAN) to study the mapping between the noise distribution and the actual sample distribution in order to extend the available dataset, and their results show that augmenting the data with GAN improves the accuracy of the diagnosis [13]. [PERSON] et al. used conditional generative adversarial networks to enable model training to generate new samples towards the constraints, resulting in higher quality new samples [14]. (2) Model level: feature enhancement extraction and integrated learning are mainly used. [PERSON] et al. proposed an Improved Active Learning (IAL) diagnostic method for the intelligent labelling of unlabelled samples with a limited number of labelled samples, showing that IAL can significantly improve the classification of unbalanced data [15]. [PERSON] et al. proposed an IGAN method for bearing fault diagnosis for a small dataset and unbalanced dataset that combines the coordinate attention mechanism to effectively mine information from a limited number of fault samples, thus increasing the diagnosis accuracy [16]. [PERSON] et al. proposed an improved channel-attention CNN for the fault diagnosis of rolling bearings; this method can be better used for the feature extraction of unbalanced data compared to other shallow models [17]. Although all the above-mentioned methods are effective in categorising and diagnosing unbalanced data, there are some limitations. (1) At the data level: most methods directly input the original signals into the network for training, which may cause information loss; there are also some 2D transformation methods that result in feature maps that may hinder feature recognition in the network. (2) At the model level: in most network structures, simple overlapping convolutional layers can recognise more features; however, in practice, network performance degradation and feature extraction is worse instead.