To address the limitations of one-dimensional vibration signals in convolutional neural networks and the insufficient feature extraction capability of traditional single data processing methods under complex operating conditions, this paper proposes a novel fault diagnosis method that integrates dual-graph transformation and an improved residual network. Firstly, the one-dimensional vibration signals are converted into time–frequency representations using the short-time Fourier transform (STFT) and the synchrosqueezed wavelet transform (SWT). Subsequently, these dual-domain representations are fed in parallel into a customized parallel two-dimensional residual network (P2D-Sk-ResNet), which incorporates the selective kernel network (SKNet) mechanism into a ResNet architecture. This design enables adaptive multi-scale feature extraction. Finally, the features from the fully connected layer are classified using the extreme gradient boosting (XGBoost) algorithm to complete the fault diagnosis task. Comparative experiments demonstrate that the proposed STFT-SWT-P2D-Sk-ResNet-XGBoost achieves a diagnostic accuracy of 98.51% under constant load conditions, significantly outperforming several baseline models. Furthermore, the model exhibits superior generalization capability under varying load conditions and strong robustness in noisy environments. The proposed method provides a valuable and practical reference for intelligent fault diagnosis in industrial applications.