基于跨类特征转移的部分转移学习方法,用于滚动轴承故障诊断.
Hongbo Que1,2, Xuyan Liu3, Siqin Jin2
1School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
本研究提出了一种使用转移学习的滚动轴承的新故障诊断方法. 它有效地处理源数据中的标签不平衡和异常类,以改善特征传输和准确诊断.
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:
- 滚动轴承故障诊断中的转移学习通常假设源域和目标域之间的样本类是一致的.
- 早期的机械应用程序可能缺乏完整的故障类数据,导致目标领域的标签不平衡.
- 在源域中没有目标域对应的异常类可以对特征对齐和转移产生负面影响.
研究的目的:
- 为滚动轴承故障诊断开发一种创新的跨类特征转移方法.
- 解决标签不平衡和异常类在转移学习场景中的挑战.
- 提高智能轴承故障诊断系统的准确性和稳定性.
主要方法:
- 拟议的方法利用标签信息来计算共享类之间的分布差异.
- 它特别规避源域的异常类的干扰.
- 该方法侧重于对部分转移任务的有效特征对齐.
主要成果:
- 在两个滚动轴承数据集上的实证评估表明,与现有方法相比,其性能优越.
- 该方法成功地减轻了异常类对特征转移的负面影响.
- 在部分转移条件下观察到故障诊断准确度的显著改善.
结论:
- 引入的跨类特征转移方法为智能轴承故障诊断提供了一种新且有效的解决方案.
- 它强大地处理标签不平衡和异常类,提高转移学习的有效性.
- 这项工作为滚动轴承的状态监测和预测性维护领域做出了宝贵的贡献.
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