Source-free domain adaptation (SFDA) has been exploited for cross-domain bearing fault diagnosis without access to source data. Current methods select partial target samples with reliable pseudo-labels for model adaptation, which is sub-optimal due to the ignored target samples. We argue that every target sample can contribute to model adaptation, and accordingly propose in this paper a novel SFDA-based approach for bearing fault diagnosis that exploits both reliable and unreliable pseudo-labels. We develop a data-augmentation-based label voting strategy to divide the target samples into reliable and unreliable ones. We propose to explore the underlying relation between feature space and label space by using the reliable pseudo-labels as ground-truth labels, meanwhile, alleviating negative transfer by maximizing the entropy of the unreliable pseudo-labels. The proposed method achieves well-balance between discriminability and diversity by taking advantage of reliable and unreliable pseudo-labels. Extensive experiments are conducted on two bearing fault benchmarks, demonstrating that our approach achieves significant performance improvements against existing SFDA-based bearing fault diagnosis methods. Our code is available at https://github.com/BdLab405/SDALR.

本研究针对当前无源领域适应方法在跨领域轴承故障诊断中忽视目标样本的问题，提出了一种新的基于标签可靠性的无源领域适应方法。通过采用数据增强的标签投票策略，我们将目标样本分为可靠和不可靠的伪标签，并利用可靠伪标签作为真实标签，显著提升了模型的适应性和性能。实验结果表明，该方法在轴承故障诊断 benchmarks 上取得了显著的性能提升。

基于标签可靠性的无源领域适应用于跨领域轴承故障诊断