Aiming at the serious negative transfer brought about by the excessive domain discrepancy of virtual-real data in the simulated data-driven transfer fault diagnosis method, this paper proposes an unsupervised fault diagnosis method based on a virtual-to-real domain multi-level joint adaption network (VDMJAN). First, the dynamic simulation model of various types of rolling bearing faults with irregular patterns is established, and the virtual signals of the faults are generated and analyzed in terms of transportability modes in combination with the fault mechanism. Then, deep convolutional neural networks with different sizes of kernels are constructed to perform feature extraction at different granularities for virtual and real domain signals, which enhances the domain invariance of the features used for state discrimination. Finally, based on the pseudo-label labeling of the simulated samples using the probabilistic fusion of parallel outputs from multiple classifiers, the samples are finely aligned with domain features at different levels. By introducing the effectiveness loss, the class-aligned measured samples are filtered and used to calibrate the classifier to better adapt to the target domain classification task. The results of experimental analysis show that the proposed VDMJAN, driven by bearing fault simulation data in a single source domain, can realize effective simulation-reality fault diagnosis and has good application prospects in the field of equipment fault diagnosis where samples are scarce in special environments.