Existing feature-based transfer learning methods have achieved great performance in the transfer fault diagnosis with unlabeled data. While most of them are global alignment methods based on maximum mean difference (MMD), which ignore the differences between different faults and pay little attention to the structural information in the unlabeled target samples. This paper proposes a transfer sparse auto-encoder (SAE) based on local maximum mean difference (LMMD) and K-means to solve the above problems. Firstly, we build a deep network based on SAE and LMMD for learning a common latent feature space where source and target subdomains are aligned. Subsequently, to fully explore the target domain information, we put forward the K-means-based method which can obtain final diagnosis results by synthesizing the source and target domain information in the latent feature space. Lastly, a case study is conducted to verify the robustness and effectiveness of the proposed methods. The experimental result demonstrates that the proposed methods outperform the MMD-based methods in the transfer fault diagnosis problem.

现有的基于特征的迁移学习方法在未标记数据的迁移故障诊断中取得了很好的效果。而大多数是基于最大均值差（MMD）的全局对齐方法，忽略了不同故障之间的差异，很少关注未标记目标样本中的结构信息。本文提出了一种基于局部最大均值差（LMMD）和K -means的传输稀疏自动编码器（SAE）来解决上述问题。首先，我们构建了一个基于 SAE 和 LMMD 的深度网络，用于学习源子域和目标子域对齐的公共潜在特征空间。随后，为了充分挖掘目标域信息，我们提出了K-基于均值的方法，通过在潜在特征空间中综合源域和目标域信息来获得最终诊断结果。最后，通过案例研究验证了所提出方法的稳健性和有效性。实验结果表明，所提出的方法在传输故障诊断问题上优于基于 MMD 的方法。