## 1 Introduction
[26] proposed CGAN-2-D-CNN, coupling data augmentation with a 2D CNN classifier. [PERSON] et al. [27] presented a hybrid GAN-capsule network to balance sample distribution and enhance feature discrimination. These developments demonstrate the increasing integration of generative modeling with traditional and deep learning-based diagnostics, offering a promising pathway for overcoming data scarcity and imbalance in real-world fault diagnosis scenarios. While existing studies have addressed the issues of data scarcity and imbalance to some extent through variants of GANs, several limitations remain:  
(1) Most current approaches primarily rely on simple statistical features derived from time-domain signals, overlooking the critical role that frequency and time-frequencydomain features play in characterizing fault modes. This single-feature dependence hinders generative networks from effectively capturing both global features and the fine-grained feature distributions associated with complex fault modes, resulting in generated samples that inadequately represent the diversity and complexity of fault signals. (2) Many existing generative networks employ traditional convolutional structures that are capable of capturing local features but are limited in modeling the cross-domain dependencies and intricate features of high-dimensional vibration signals. This limitation becomes particularly pronounced in the case of multimodal faults, where the generated samples fail to reflect the complex interdependencies of real-world signals. (3) Most GANs rely on conventional loss functions (e.g., Jensen-Shannon divergence), which are susceptible to issues such as gradient vanishing and mode collapse during adversarial training, leading to inconsistencies in the quality of generated samples. Moreover, the inherent conflict between the discriminator's classification task and its adversarial discrimination role further compromises both the quality and diversity of the generated samples. To address the aforementioned challenges, we propose a Multi-Domain Feature Transformer Generative Adversarial Network (MDFT-GAN). This framework leverages multi-domain feature fusion, an enhanced network structure, and an improved training mechanism to significantly enhance the quality and diversity of generated samples, overcoming the limitations of existing methods. The main contributions of this paper are as follows:  
(1) We introduce a multi-domain information fusion strategy that effectively combines time-domain, frequency-domain, and time-frequency domain features, capitalizing on their complementarity. This approach provides comprehensive feature support for generating complex fault modes, thereby improving the completeness and diversity of the generated samples. (2) To enhance the generative network's ability to model both global dependencies and local features, we design a network structure that combines convolutional layers with a Transformer encoder. A multi-head self-attention mechanism and channel attention are incorporated to refine the quality and feature representation of the generated samples. Additionally, we introduce an adversarial loss based on Wasserstein distance, supplemented with a gradient penalty mechanism, to significantly improve training stability while maintaining an auxiliary classification loss. (3) We conduct a comprehensive comparative analysis using two publicly available bearing fault datasets. The results demonstrate the superiority of MDFT-GAN in terms of both the quality and diversity of the generated samples. Experimental findings further confirm that the proposed method outperforms mainstream generative adversarial networks, exhibiting higher accuracy and robustness in fault diagnosis tasks. (4) To enhance the transparency and interpretability of the diagnostic process, we further incorporate a Grad-CAM-based visual interpretability framework.