条件分布域适应下数模混动齿轮箱故障诊断

Fault diagnosis of gearbox is crucial for ensuring the reliability, safety and economic feasibility of meclianical Systems. In industrial practice, gearboxes usually operate under normal conditions, fewer fault states appear. Due to higher cost of obtaining labeled fault data, health monitoring of gearboxes faces the problem of scarce labeled fault data. However, existing deep transfer diagnosis methods have limitations of uneven data generation quality and excessive reliance on minority dass Information. Here, to overcome this challenge, a fault diagnosis method for digital-analog hybrid transmission gearbox under conditional distribution domain adaptation was proposed. Firstly, based on the lumped parameter method, dynamic models of different gear faults were constructed to expand fault data in less labeled source domain. Secondly, the class-conditional maximum mean discrepancy (CMMD) was embedded into diagnosis model, and the relation between fault features and fault labels was explicitly constructed in reproducing kernel Hubert space (RKHS) to reduce distribution differences between source domain data and target domain data. At the same time, to ensure establishing reliable pseudo labels for target domain samples, entropy loss was introduced into model training process. Finally, the effectiveness and feasibility of the proposed method were verified with two experiments. Diagnostic model to explicitly establish the relationship between fault features and fault labels in the reproducing kernel hilbert space (RKHS), reducing the distribution discrepancy between the source and target domain data. Meanwhile, to ensure reliable pseudo-labels for target domain samples, entropy loss is introduced during model training. Finally, the effectiveness and feasibility of the proposed method are validated through two experiments.