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A single-vibration signal is no longer adequate to fulfill the requirements of intelligent fault diagnosis (IFD) of bearings in complex systems. With the rapid advancement of the industrial Internet of Things, IFD methods based on multimodal information fusion have gained popularity. Acoustic signals are noninvasive, easily captured, and have a wide monitoring range. Therefore, acoustic-vibration fusion IFD (AVFIFD) holds promising application prospects. Nevertheless, current AVFIFD methods suffer from two limitations that lead to reduced accuracy: insufficient consideration of both local and temporal features during the feature extraction process, and inadequate emphasis on the correlation between acoustic and vibration features. To overcome these limitations and enhance the accuracy of AVFIFD, we propose the convolution and cross-fusion transformer (CCFT), which combines convolution and transformers to enhance local and temporal feature extraction and introduces cross-fusion transformers to improve the correlation between acoustic and vibration features. Finally, fault type identification is accomplished through a fusion classification module. In two case studies, CCFT outperforms other fusion methods. Additional visualization analysis illustrates that the cross-fusion transformer can improve the correlation of fault information by progressively minimizing the discrepancies between acoustic and vibration feature representations at each layer. more...