一种用于跨机器轴承故障诊断的新方法,基于信封频谱和有条件的度量学习
Xu Yang1, Junfeng Yang2, Yupeng Jin3
1School of Intelligent Manufacturing, Nanyang Institute of Technology, Nanyang 473004, China.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
这项研究引入了一种新的轴承故障诊断方法,使用信封频谱和条件度量学习. 它通过提取共同特征和解决域差异来提高不同设备的准确性.
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:
- 轴承故障诊断的准确性随着不同设备的数据而下降.
- 现有的无监督域调整方法经常忽略关键的歧视性信息.
- 这种限制限制了跨设备故障诊断模型的性能.
研究的目的:
- 提出一种先进的轴承故障诊断方法,克服跨设备数据差异.
- 通过利用标签分发的歧视性信息来提高诊断准确性.
- 提高故障诊断模型的融合和培训效率.
主要方法:
- 使用信封光谱分析进行强大的频域特征提取.
- 开发一个深层的罗卷积神经网络,采用有条件的度量学习.
- 实施动态加权因子以优化模型融合和培训.
主要成果:
- 拟议的方法在12个跨设备故障诊断任务中表现出卓越的性能.
- 它在所有三个关键评估指标上都超过了现有的相关方法.
- 有效地消除了源域和目标域之间的数据分布差异.
结论:
- 这种新方法在跨设备场景中显著提高了轴承故障诊断的准确性.
- 条件度量学习和信封频谱分析对于域调整是有效的.
- 该方法为现实世界工业设备监控提供了一个有希望的解决方案.
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