在未见条件下对轴承故障诊断的不确定性域扰乱
IEEE transactions on cybernetics
|July 4, 2025
概括
这项研究引入了新的策略,用于故障诊断,当目标数据是不可用的. 新的方法捕获源域之外的故障信息,增强对未见条件的模型概括性.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 工程 工程师 工程师 工程师
背景情况:
- 域调整 (DA) 在跨域故障诊断方面表现出色,但需要目标数据.
- 现有的故障诊断领域概括方法的性能改进有限.
- 当前的方法往往无法捕获源域分布之外的故障信息.
研究的目的:
- 开发用于故障诊断的高级域泛化技术.
- 解决当目标域数据无法访问时现有方法的局限性.
- 为了提高模型概括性能,用于看不见的故障条件.
主要方法:
- 拟议的乘法噪声高斯扰动策略来模拟域转移不确定性.
- 引入了添加噪音线性融合策略,以确保生成的特征风格的真实性.
- 综合特征统计与随机凸重为可靠性和多样性.
主要成果:
- 拟议的策略有效地捕获了超出源域分布的故障信息.
- 生成的多样化的功能样式提高了网络学习故障信息的能力.
- 在公开和真实数据集上的实验结果表明了显著的有效性.
结论:
- 这种新的方法可以提高故障诊断概括性能,而无需目标域数据.
- 扰动和融合策略的结合为跨领域故障诊断提供了强大的解决方案.
- 这项工作推进了对工业故障诊断应用的域概括领域.
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