基于转移学习的机械轴承故障诊断的研究
Xinjian Gao1, Yizhi Zhang1, Enzhi Dong1
1Shijiazhuang Campus of Army Engineering University of PLA, Shijiazhuang 050003, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
这项研究开发了一个转移学习系统,用于机械轴承的智能故障诊断. 转移学习模型显著提高了对新数据的诊断准确性,优于其他方法.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 智能故障诊断对于机械轴承健康监测至关重要.
- 实验室和现实世界数据之间的域分布差异限制了诊断模型的适用性.
研究的目的:
- 为机械轴承构建一个强大的转移学习诊断系统.
- 为了应对故障诊断领域转移的挑战.
- 提高诊断模型的准确性和适用性.
主要方法:
- 利用希尔伯特变换来提取时间域,频域和周期性相关性特征.
- 实施并比较了三个分类模型:转移学习 (TL),梯度增强机 (GBM) 和随机森林 (RF).
- 在模型解释性和特征重要性分析中使用了SHapley添加式解释 (SHAP).
主要成果:
- 转移学习 (TL) 获得了最高的准确性 (F1得分为0.9631).
- 在一个转移任务中,TL达到97.6%的分类准确度,与直接源域模型应用 (70.3%) 相比增加了27.3个百分点.
- SHAP分析为TL模型的决策过程和特征重要性提供了洞察力.
结论:
- 开发的转移学习系统在机械轴承故障诊断方面表现出卓越的性能.
- 该系统有效地克服了域分布差异,提高了诊断模型的可靠性.
- 使用SHAP的特征重要性分析验证了该模型的决策依据.
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