Transfer learning method for rolling bearing fault diagnosis under different working conditions based on CycleGAN
Transfer learning method for rolling bearing fault diagnosis under different working conditions based on CycleGAN
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DOI：
10.1088/1361-6501/ac3942
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发表时间：
2021-11
影响因子：
2.4
通讯作者：
Jiantong Zhao;Wentao Huang
中科院分区：
文献类型：
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作者：
Jiantong Zhao;Wentao Huang
在实际的轴承故障诊断任务中，可用的标记数据往往不是来自待诊断设备，也不能涵盖所有的工作条件。所采用的数据驱动方法要求具有一定的跨领域、跨工况迁移学习诊断能力。然而，受现有迁移学习方法性能的限制，源域和目标域之间的潜在差异给迁移诊断的准确性带来了挑战。本文提出了一种基于循环生成对抗网络（CycleGAN）和动力学模型的跨工况数据补充方法，该方法可以利用有限的可用数据来逼近现有数据的缺失部分，并用于目标域的诊断。首先，以有限的实验数据为目标域，以工况对应的仿真数据为源域，以工况为基准约束两个数据集之间的数据对应关系。然后使用CycleGAN模型学习从仿真到实验的特征映射。其次，根据待测数据的工作状态，将相应的仿真数据输入训练好的生成器中，得到相应工作状态下具有实验特征的标记数据，并将数据集作为源域数据传递给待测数据。在自制仿真和实验数据集的测试中，结合基于概率分布自适应的迁移学习方法，表明当工况跨度较大时，所提方法能有效提高单一迁移学习方法在跨域、跨工况下的诊断效果。
In practical bearing fault diagnosis tasks, the available labeled data are often not from the equipment to be diagnosed and cannot cover all manner of working conditions. The adopted data-driven method is required to have a certain degree of cross-domain and cross-working condition transfer learning diagnosis ability. However, limited by the performance of existing transfer learning methods, the potential difference between the source domain and the target domain poses a challenge for the accuracy of transfer diagnosis. In this paper, a cross-working condition data supplement method based on the cycle generative adversarial network (CycleGAN) and a dynamics model is proposed, which can use limited available data to approximate the missing parts of existing data and be used for diagnosis of the target domain. First, we considered the limited experimental data as the target domain, the simulation data corresponding to the working condition as the source domain and used the working condition as the benchmark to constrain the data correspondence between the two datasets. We then used the CycleGAN model to learn the feature mapping from simulation to experiment. Second, based on the working condition of the data to be tested, the corresponding simulation data were input into the trained generator to obtain labeled data with experimental characteristics under the corresponding working conditions, and transferred the dataset as the source domain data to the data to be tested. In the test using self-made simulation and experimental datasets, combined with the transfer learning method based on the probability distribution adaptation, it was shown that the proposed method could effectively improve the diagnostic impact of the single transfer learning method in cross-domain and cross-working conditions when the working condition span was large.