A Novel Transfer Dictionary Learning Strategy for Rolling Bearing Fault Identification With a Mixed Noise Model

Rolling bearings are an essential core component in the power transmission systems of rotating machinery. Many previous dictionary learning (DL) methods have proven to be powerful for rolling bearing fault diagnosis. Existing diagnostic methods based on DL usually involve analyzing training and test data drawn from the same distribution. However, an unbalanced data distribution is a common phenomenon in practice. In addition, the signals in the source and target domains are often mixed with noise and irrelevant information in complex environments, which greatly reduces the performance of DL. Therefore, a new DL method based on a mixed noise model for transfer DL (TDL-MN) is presented. Specifically, the TDL-MN model is constructed to overcome noise and irrelevant signal interference in the DL process under variable conditions. Moreover, rolling bearing health status is diagnosed through sparse representation (SR) classification by calculating the target domain samples corresponding to the redundancy error in the SR vector. The results for two cases verify the ability of the TDL-MN method to accurately identify bearing fault types under complex and variable working conditions. Comparisons with other methods verify the applicability and superiority of the proposed method.