一个基于注意力的多维故障信息共享框架,用于轴承故障诊断
Yunjin Hu1, Qingsheng Xie1, Xudong Yang2
1Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550028, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
这项研究引入了轴承故障诊断的新框架,提高了小样本环境中的准确性. 基于注意力的多维故障信息共享 (AMFIS) 框架增强了故障定位和量化.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度学习在特征提取方面表现出色,但在故障诊断方面却在有限的数据上扎.
- 实用工程需要多维故障信息 (局部化和量化),而当前的方法往往缺乏这些信息.
研究的目的:
- 为了解决小样本大小的限制以及在轴承故障诊断中需要多维故障信息的需求.
- 提出一个基于注意力的多维故障信息共享 (AMFIS) 框架.
主要方法:
- 一个共享网络捕获了用于故障定位 (FLT) 和量化 (FQT) 的常识.
- 具有注意力机制 (AM) 的分支网络过相关特征以进行小样本增强.
- 动态调整策略 (DAS) 适应调节训练重量以获得最佳结果.
主要成果:
- 在克服小样本挑战方面,AMFIS框架表现出有效性.
- 在两个案例中的实验结果验证了拟议方法的优越性.
结论:
- 在数据稀缺的情况下,AMFIS框架为多维轴承故障诊断提供了强大的解决方案.
- 共享网络,注意力机制和动态调整策略的整合大大提高了诊断能力.
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