Domain Adaptation-Based Transfer Learning for Gear Fault Diagnosis under Varying Working Conditions

The nonidentical and nonindependent distribution problem, caused by various working conditions of gears, inevitably makes the effectiveness of gear fault diagnosis (GFD) degrade because of insufficient or low-quality data. Although some domain adaptation learning (DAL) models exist, most of them only aim at minimizing the global distributional mean discrepancy between the source domain (SD) and the target domain (TD), while the contribution of individual data is neglected. To address this issue, a new DAL, aiming to reduce the discrepancy on extracted features under a least square support vector machine (LSSVM) framework, is studied to exploit SD signals from another working conditions or adjacent mechanical parts to assist and boost target gear fault diagnostic performance in this article. In addition, the white cosine similarity criterion is adopted to quantize the distributional weights of SD and TD feature data, and then the weights are added into a regularization function that measures the projected distributional discrepancy in the LSSVM framework. Related experiments prove that the proposed method has higher diagnostic accuracies than other models. So, this strategy is expected to be a useful tool to transfer from SD to TD, and to boost the GFD performance under various working conditions.