基于动态模拟和部分转移学习的行星变速箱故障诊断
Mengmeng Song1, Zicheng Xiong2, Jianhua Zhong2
1College of Information, Mechanical and Electrical Engineering, Ningde Normal University, Ningde 352000, China.
Biomimetics (Basel, Switzerland)
|August 25, 2023
概括
这项研究通过使用转移学习来弥合模拟和现实世界的数据差距来增强行星变速箱故障诊断. 这种新方法通过过无关的模拟数据来提高诊断准确性,优于现有技术.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 对于行星变速箱故障诊断的深度学习需要大量的现实世界数据,这往往是不够的.
- 当模拟数据标签空间超过现实世界数据标签空间时,现有的转移学习方法扎,导致诊断不准确.
研究的目的:
- 为行星变速箱故障诊断开发一种改进的转移学习方法,即使标签空间不同,也有效利用模拟数据.
- 通过减轻无关模拟数据类别的干扰来提高故障诊断的准确性和稳定性.
主要方法:
- 引入多个域分类器和加权学习方案,用于现有的域对抗转移学习.
- 开发了一种方法来评估模拟数据的可转移性和适应性测量对标签预测器和域分类器的贡献.
- 从无关的模拟数据类别中过干扰,以实现与现实数据的准确匹配.
主要成果:
- 实验结果证明了拟议方法在行星变速箱故障诊断中的有效性.
- 与现有的转移学习方法相比,拟议的方法实现了更高的诊断准确性.
- 成功地解决了模拟和现实数据之间的不同标签空间的挑战.
结论:
- 新型转移学习方法显著提高了行星变速箱故障诊断的准确性.
- 该方法提供了一个强大的解决方案,用于利用模拟数据,当现实数据有限或有不同的标签分布时.
- 这项工作为机械系统的状态监测和预测性维护提供了宝贵的进步.
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