A Small-Sample Fault Diagnosis Method for Rolling Bearings Based on Balanced Distribution Adaptation and Support Vector Machine

To address the issue of low diagnostic accuracy caused by distribution differences between source and target domains in rolling bearing fault diagnosis, this study proposes a method combining Balanced Distribution Adaptation (BDA) and Support Vector Machines (SVM). The approach utilizes BDA to simultaneously minimize discrepancies in both edge and conditional distributions between domains, enabling effective feature alignment and enhancing the model's cross-domain generalization in small-sample scenarios. After extracting time and frequency-domain features, BDA adaptively adjusts feature distributions, and SVM is employed for fault classification. Experimental results demonstrate that the BDA-SVM method achieves over 94% diagnostic accuracy, showcasing strong performance and robustness for bearing fault diagnosis. Compared with traditional SVM and other methods without transfer learning, the proposed approach shows significant improvement in diagnostic accuracy under cross-domain conditions.