非理想数据下基于仿真数据辅助迁移学习的滚动轴承故障诊断

旋转机械在实际工程应用中常处于正常状态，因而呈现故障样本稀少甚至部分缺失等非理想数据情况。针对直接采用非理想数据建立深度学习诊断模型时的低准确率问题，提出基于有限元仿真数据辅助迁移学习的故障诊断方法。首先，通过数值仿真计算不同运行工况和故障类型的轴承信号；进而，利用大量低成本高保真的仿真样本对模型预训练，利用真实小样本或者仿真样本增补后的混合样本进行模型微调，以完成高准确率故障诊断，并降低迁移学习对故障轴承实测数据的依赖；最后，利用两个轴承实验台数据进行验证。结果表明在单类故障样本数为1时，采用所提方法建立的模型准确率超过95%;在故障样本稀少且多类缺失时，准确率比仿真数据直接增补方式提升超10%。

Rolling bearing fault diagnosis for non-ideal dataset based on finite element simulation and transfer learning

In practical engineering applications, rotating machinery typically operates under normal conditions, which can result in nonideal datasets with few or even partially missing fault samples. To address the low accuracy issue in deep learning diagnosis models trained directly on non-ideal datasets, a fault diagnosis method that incorporates finite element simulation to facilitate transfer learning is proposed. Firstly, vibration signals with different operating conditions and fault types are derived via numerical simulations. Subsequently, a large number of cost-effective and high-fidelity simulation samples are employed to pre-train a diagnostic model, and the authentic limited dataset or the hybrid dataset augmented by simulation samples is employed to fine-tune the pre-trained diagnostic model. This approach aims to implement high-precision fault diagnosis and mitigate the reliance on actual or experimental fault data. Finally, two bearing datasets are used to evaluate the effectiveness of the proposed method. Results show that the diagnostic model constructed via the proposed method achieves an accuracy exceeding 95% with a sample size of one for each fault category. In addition, in cases where the fault samples are limited and certain types of faults are missing, the accuracy is boosted by over 10% compared to the approach of supplementing the simulation samples directly.