Bearing fault diagnosis method and system based on improved LSSVM transfer learning

The invention discloses a bearing fault diagnosis method and system based on improved LSSVM transfer learning, and the method comprises the following steps: processing target data and auxiliary data through employing recurrence quantification analysis, extracting a nonlinear feature and combing the nonlinear feature with a conventional time domain feature, forming a characteristic vector, and forming a training set; constructing a fault classification model through employing an improved LSSVM transfer learning, extracting the nonlinear feature of unmarked fault vibration data of a target bearing under a target work condition through the recurrence quantification analysis, combining the nonlinear feature with the conventional time domain feature, forming a feature vector, forming a test set, inputting the test set into a trained improved LSSVM model, carrying out analysis and outputting a result. Through respectively adding a penalty function and constraint condition of an auxiliary set into the original target function and constraint condition, the method enables the improved LSSVM to be affected by the auxiliary set in an iterative learning process, and improves the classification precision.