Accurate identification of rolling bearing faults is quite significant for the stable operation of mechanical systems. However, for practical diagnosis issues, it is difficult to obtain abundant labeled data due to the change of operating conditions and complex working environment, which puts forward higher requirements on the ability of the diagnosis methods. To tackle the mentioned problem, a novel transfer learning method based on a little labeled data is proposed, which uses bidirectional gated recurrent unit (BiGRU) and Manifold Embedded Distribution Alignment (MEDA). Firstly, frequency spectrum datasets are utilized to remove the redundant information of raw vibration signals. Secondly, the BiGRU network is constructed to generate auxiliary samples that are utilized as source domain. Finally, MEDA, as the most powerful non-deep transfer learning method, is applied to align the distribution of these auxiliary samples generated by BiGRU and the unlabeled samples from target domain. Experiment results indicate the excellent performance of the proposed method under a little labeled data.

准确识别滚动轴承故障对机械系统的稳定运行非常重要。但是，对于实际的诊断问题，由于工作条件的变化和复杂的工作环境，难以获得丰富的标记数据，这对诊断方法的能力提出了更高的要求。为了解决上述问题，提出了一种基于少量标记数据的新型转移学习方法，该方法使用双向门控递归单元（BiGRU）和歧管嵌入式分布对准（MEDA）。首先，利用频谱数据集去除原始振动信号的冗余信息。其次，构建BiGRU网络以生成用作源域的辅助样本。最后，MEDA 作为最强大的非深度转移学习方法，被用于对齐BiGRU生成的这些辅助样本和目标域中未标记样本的分布。实验结果表明，该方法在少量标记数据下具有优异的性能。