| 张星泽,赵爽耀,温创辉,闫生茂,蔡正阳.基于属性引导细粒度特征对比的小样本跨域故障诊断方法[J].电子测量与仪器学报,2025,39(12):19-33 |
| 基于属性引导细粒度特征对比的小样本跨域故障诊断方法 |
| Small-sample cross-domain fault diagnosis method based onattribute-guided fine-grained feature contrast |
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| DOI： |
| 中文关键词: 小样本跨域故障诊断 细粒度故障诊断 有监督对比学习 多属性学习 多任务学习 |
| 英文关键词:small sample cross-domain fault diagnosis fine-grained fault diagnosis supervised contrastive learning multi-attribute learning multi-task learning |
| 基金项目:国家自然科学基金青年项目(72201087)资助 |
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| Author | Institution |
| Zhang Xingze | School of Management, Hefei University of Technology, Hefei 230009,China |
| Zhao Shuangyao | 1.School of Management, Hefei University of Technology, Hefei 230009,China; 2.Key Laboratory of Process
Optimization and Intelligent Decision Making, Ministry of Education, Hefei 230009,China |
| Wen Chuanghui | School of Management, Hefei University of Technology, Hefei 230009,China |
| Yan Shengmao | School of Management, Hefei University of Technology, Hefei 230009,China |
| Cai Zhengyang | 1.School of Management, Hefei University of Technology, Hefei 230009,China; 2.Key Laboratory of Process
Optimization and Intelligent Decision Making, Ministry of Education, Hefei 230009,China |
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| 中文摘要: |
| 在实际工业场景中,由于故障数据难以获取,且变工况条件下新的细粒度故障模式不断涌现,小样本变工况下跨域故障诊断的需求逐渐凸显。然而,在大规模细粒度故障分类情境下,当前小样本跨域故障诊断方法存在特征甄别能力弱、模型泛化效果差、新类别识别困难等短板。为此,提出了一种基于属性引导细粒度特征对比学习的小样本跨域故障诊断方法,在多任务学习方法的基础上整合有监督对比学习和多属性学习,通过属性学习过程引导故障诊断模型有效区分源域已知细粒度故障特征,同时基于提取的跨类别属性语义信息，采用少样本微调的方式实现目标域未知新故障的精准识别。通过对比各故障诊断方法在少样本、多分类轴承故障数据集上的性能表现,验证了所提方法在小样本细粒度情境下具有优越的跨域诊断性能。 |
| 英文摘要: |
| In real industrial scenarios, due to the difficulty in obtaining fault data and the continuous emergence of new fine-grained fault modes under variable operating conditions, the demand for small-sample cross-domain fault diagnosis under variable operating conditions has gradually come to the fore. However, in the context of large-scale fine-grained fault classification, the current small-sample cross-domain fault diagnosis methods have shortcomings such as weak feature screening ability, poor model generalization, and difficulty in identifying new categories. To this end, this paper proposes a small-sample cross-domain fault diagnosis method based on attribute-guided fine-grained feature comparison learning, integrating supervised contrastive learning and multi-attribute learning on the basis of multi-task learning method, guiding the fault diagnosis model to efficiently differentiate the known fine-grained fault features in the source domain through the attribute learning process, and realizing the accurate identification of unknown new faults in the target domain based on the extracted semantic information on cross-category attributes and the fine-tuning of fewer samples. In this paper, by comparing the performance of each fault diagnosis method on the sample-small, multi-classification bearing fault dataset, we verify that the proposed method has superior cross-domain diagnosis performance in small-sample fine-grained scenarios. |
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