Abstract: A deep partial transfer method weighted by a domain asymmetry factor for rolling bearing fault diagnosis includes: first, extracting the deep transfer fault features from the monitoring data of the source rolling bearing and the target rolling bearing by a deep residual network; second, training the domain confusion network by using the deep transfer fault feature, and calculating the domain asymmetric factor; next, calculating the maximum mean discrepancy implanted by a multiple polynomial kernels of the fault features of the adaptation layer of the deep residual network, and using the domain asymmetry factor weighting to suppress the contribution of outlier fault features of the source rolling bearing; and finally, building the objective function using the weighted maximum mean discrepancy implanted by the multiple polynomial kernels to train the deep residual network.