Lei Tongfei, Pan Feng, Hu Jiabei, He Xu, Li Bing
School of Mechanical Engineering, Xijing University, Xi'an, 710123, China.
Xi'an Aeronautical Computing Technique Research Institute, Xi'an, 710068, China.
Sci Rep. 2025 Mar 28;15(1):10793. doi: 10.1038/s41598-025-88353-1.
The closed-set assumption often fails in practical industrial applications, especially considering diverse working conditions where the data distribution may differ significantly. In light of this, a domain adaptation method with adversarial learning is designed for open-set fault diagnosis. Firstly, convolutional autoencoder is developed to distill the fault features; Secondly, an unknown boundary by weighting the similarity between known and unknown classes is established, to ensure shared class alignment between domains while classifying known classes across domains and identifying unknown fault samples. Finally, the diagnostic performance is evaluated using three sets of rolling bearing datasets. The proposed method achieved average diagnostic F1-scores of 96.60%, 96.56%, and 96.62% on these datasets, respectively. The results demonstrate that the method effectively rejects unknown fault data in the target domain while aligning known classes, validating its fault diagnosis capability under the open-world assumption.
封闭集假设在实际工业应用中常常失效，尤其是考虑到数据分布可能存在显著差异的各种工作条件时。鉴于此，设计了一种基于对抗学习的域适应方法用于开放集故障诊断。首先，开发卷积自动编码器以提取故障特征；其次，通过加权已知类和未知类之间的相似度来建立未知边界，以确保在跨域分类已知类并识别未知故障样本时，各域之间共享类对齐。最后，使用三组滚动轴承数据集评估诊断性能。该方法在这些数据集上分别实现了96.60%、96.56%和96.62%的平均诊断F1分数。结果表明，该方法在对齐已知类的同时有效地拒绝了目标域中的未知故障数据，验证了其在开放世界假设下的故障诊断能力。
