一种基于改进的域调整的智能轴承故障转移诊断方法
Jinli Che1, Liqing Fang1, Qiao Ma1
1Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China.
Entropy (Basel, Switzerland)
|November 26, 2025
概括
本研究介绍了用于滚动轴承故障诊断的先进领域适应方法. 它通过使用多层MMD和对抗分类来调整不同数据集的特征来提高跨领域的性能.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 滚动轴承的跨领域故障诊断在特征转移方面面临挑战.
- 现有的方法通常在源域和目标域之间存在显著的分布差异.
研究的目的:
- 提出一个增强的领域适应方法,用于滚动轴承的智能故障诊断.
- 提高故障诊断模型的跨领域适应性和诊断性能.
主要方法:
- 使用一维卷积神经网络 (1D-CNN) 来进行特征提取.
- 使用多层多核最大平均差异 (ML-MK MMD) 来量化和减少特征分布差异.
- 集成的ML-MK MMD,具有对抗域分类,用于增强域调整.
主要成果:
- 提出的方法有效地提取域不变特征.
- 显著减少了源域和目标域之间的分布差距.
- 在目标域样本上证明了增强的跨域诊断性能.
结论:
- 增强的域调整方法成功地解决了跨域故障诊断中的特征传输挑战.
- 结合ML-MK MMD和对抗域分类,可以实现更高的诊断准确性.
- 这种方法为在各种操作条件下进行智能故障诊断提供了强大的解决方案.
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