Traditional fault diagnosis methods mainly rely on a single sensor signal, such as vibration or generator current signals, thus it often leads to limited diagnosis accuracy, primarily when multiple faults exist at the same time. Considering the electromechanical coupling characteristics of the electromechanical drive system, different sensors usually contain correlated and complementary information, which can improve the diagnosis performance. To this end, this article proposes a current-aided vibration fusion network (CAVFNet) to diagnose different faults in the electromechanical drive system. The raw vibration and current signals are decomposed via wavelet packet decomposition (WPD) into time-frequency matrices representing fault information in different frequency bands. Meanwhile, a current-aided fusion module (CAFM) is designed to achieve sufficient fusion of cross-modal information. Reweighting the fused features in spatial dimensions uses the excitation maps extracted from the current signals. Finally, an adaptive decision-level fusion strategy is developed to integrate information from different branches. Experimental results on both datasets demonstrate our proposed method has strong robustness and high diagnostic performance. The core code for this project is available at: https://github.com/LKLaii/project-CAVFNet.