Abstract: An broad transfer learning algorithm based on angular domain resampling(ADR-BTL) is proposed for bearing fault diagnosis in response to the fact that the signals collected in variable operating conditions are usually non-stationary signals and the distribution of the collected vibration data is inconsistent. Firstly, the non-stationary time domain vibration signal is converted into an angular domain stationary signal, and the smoothed signal is feature extracted to construct a multi domain feature dataset. Then, the source domain data and target domain data under different operating conditions are domain adapted using the balanced distribution adaptation (BDA) method to reduce the distribution differences between domains. Finally, a broad transfer learning model is constructed. The experiment first verified that the angular domain resampling method can smooth the vibration signal, and then through simulation sample analysis, it was found that the BDA method can solve the problem of inconsistent data distribution. Finally, the experimental results showed that the proposed ADR-BTL recognition rate reached 98.9%, and the recognition effect was the best, proving that the proposed method is effective in bearing fault diagnosis.