具有分布相似性的元学习 偏好在不同工作条件下对几次射击故障诊断
IEEE transactions on cybernetics
|December 22, 2023
概括
通过使用元学习与新的任务生成策略来改进少量射击故障诊断. 这种方法增强了不同条件下的复杂系统的域适应性和概括性.
科学领域:
- 工程 工程师 工程师 工程师
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 由于有限的注释数据和复杂系统中的不同工作条件,短暂的故障诊断面临着挑战.
- 超级学习提供了一个有前途的解决方案,但需要在域调整和概括方面进行改进.
研究的目的:
- 通过提高元学习对分布转移的稳定性来提高短时间故障诊断的域名适应性和概括性.
- 提出一个无监督的跨任务元学习策略,偏好分布式相似性用于故障诊断.
主要方法:
- 一个新的元任务生成策略,优先考虑源和目标实例之间的分布相似性,使用分布-距离权重机制.
- 应用最大平均差异 (MMD) 来量化分布距离和模型不可知的元学习 (MAML) 来诊断故障.
- 使用无监督的跨任务元学习,通过不同的工作条件自然地解决分配转移.
主要成果:
- 拟议的战略在不同的工作条件下显著优于现有的少数射击故障诊断方法.
- 在提高元学习模型的域调整和概括能力方面表现出有效性.
- 对两个公共轴承故障诊断数据集的验证证实了该方法的稳定性和性能.
结论:
- 当将分布相似性特征纳入任务生成时,Meta-learning提供了一种有效的方法,用于几次拍摄的故障诊断.
- 开发的无监督元学习策略增强了对分布转移的稳定性,这对现实世界工程系统至关重要.
- 这项研究推动了对复杂系统诊断的meta-learning的应用,在具有挑战性的操作可变性下.
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