As an important core part of machine tools,rolling bearings plays an important role in ensuring the normal operation of machine tools.However,in the actual working environment,the working condition of machine tools often changes according to different working requirements,which will have a certain impact on the speed and load of machine tool bearings,resulting in the mechanical vibration signal of the bearing showing non-stationarity,nonlinear and non-periodic characteristics.At present,the bearing fault diagnosis methods based on deep learning are data-dependent,requiring the training (source domain) and test (target domain) data sets to have the same data characteristics and the presence of sufficient labeled data with fault information.However,since machine tools operate under non-stationary conditions,the training model built on one condition cannot be directly applied to another condition.In order to solve this problem,based on transfer learning (TL) technology,a model combining one-dimensional convolutional neural network (1-DCNN) and transfer learning was designed.In this model,one-dimensional convolutional network was used to extract the fault feature information directly from the original vibration signal,and the common features of the two domains were extracted by adversarial strategy migration.The difference measurement of domain distribution was used to narrow the feature distribution of the two domains and realize the fault diagnosis of bearing migration across working conditions.Finally,12 groups of migration tasks were constructed to verify the superiority of the designed model.The results show that the designed transfer learning neural network model based on one-dimensional convolution can directly achieve real-time monitoring of machine tool bearing faults.The designed model greatly improves the performance of migration fault diagnosis by combining adversarial strategy transfer and measurement domain distribution difference transfer strategies,which can better extract common features of the source and target domains.Among the 12 migration tasks constructed in the experiment,it is superior to the other two migration strategies and can perfectly complete the migration fault diagnosis task.