## 3 The Proposed Method
### The Design of the Data Augmentation Model  
Although ACGAN offers certain advantages in ensuring sample category consistency, it has significant limitations when dealing with complex working condition data: (1) it struggles to effectively capture both the global and local characteristics of the data; (2) under conditions of severe sample category imbalance, the generated samples fail to fully represent the characteristics of the real data. These limitations are particularly pronounced in complex multi-category fault scenarios, where the inability to accurately model the distribution of different categories in the generated samples reduces the robustness of the classification model. To address these shortcomings, this paper introduces MDFT-GAN, designed to generate high-resolution, high-fidelity samples that meet the generation requirements of multi-class complex fault data. The structure of MDFT-GAN is illustrated in Figure 4.  
Figure 3: MDFT-GAN-based overall framework. The generator input consists of two components: (a) a noise vector, which is a randomly sampled latent vector used to introduce diversity in the generated samples, and (b) a category label vector, which is mapped to a high-dimensional feature vector through an embedding layer. The parameter matrix of the embedding layer is defined as follows:  
\[\mathbf{W}_{\text{embed}}\in\mathbb{R}^{n_{\text{class}}\times d_{\text{emb}}} \tag{9}\]  
where \(n_{\text{class}}\) is the number of categories and \(d_{\text{emb}}\) is the embedding dimension. The noise vector \(Z\) and category vector \(\mathcal{C}\) are element-wise multiplied to form joint features, which are then converted into a four-dimensional tensor. This tensor serves as the initial input to the MDFT module in the generator. The MDFT module aims to construct an efficient and generalizable generative feature representation framework. By synergizing convolutional operations with a Transformer encoder, the module introduces a multi-domain feature modeling strategy, achieving a deep integration of local details and global information, rather than merely stacking modules. The MDFT module first extracts local features and retains spatial information using 2D convolution. Simultaneously, it innovatively integrates an attention mechanism with dynamic feature reallocation to address the challenge of high-dimensional feature interaction in complex data distributions. Specifically, after convolutional feature extraction, the MDFT module introduces MHA, which dynamically adjusts feature weights between different channels according to the uneven distribution of global features. This approach not only captures the global dependencies of the generated features but also enhances the expressiveness of sparse features by optimizing attention distribution. This addresses the limitations of traditional attention mechanisms in processing high-dimensional data. Furthermore, the module incorporates ECA to refine feature interactions between channels. This not only improves the consistency of the generated samples under different category conditions but also significantly enhances feature representation in multi-class imbalance scenarios. In addition, the MDFT module employs a unique normalization and feature mapping strategy to address the issue of modal mismatch in multi-domain feature fusion. The module utilizes multi-level feature modeling to efficiently capture both the global and local distributions of complex signals, providing a strong conditional constraint for the category  
Figure 4: Structure of MDFT-GAN. relevance of generated samples. This design transcends traditional module stacking by seamlessly integrating feature extraction, enhancement, and redistribution, ensuring the stability and adaptability of MDFT-GAN in generating high-quality samples. For an input feature \(X\in\mathbb{R}^{B\times C\times H\times W}\), each channel feature is first compressed along the spatial dimension using Global Average Pooling (GAP) to obtain a channel feature vector \(Y\).