滚动轴承的新故障诊断方法基于改进的剩余收缩网络与转移学习相结合
1School of Mechanical Engineering, Xinjiang University, Urumqi 830046, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
这项研究引入了一个具有转移学习的适应性残留收缩网络,用于有效的滚动轴承故障诊断,即使数据有限和噪音条件低. 该方法可提高在可变负载和速度下诊断的准确性.
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:
- 滚动轴承故障诊断对于工业设备的可靠性至关重要.
- 有限的故障数据和噪声阻碍了传统的诊断方法,特别是卷积神经网络.
- 现有的方法难以应对变化的操作条件,例如负载和速度的变化.
研究的目的:
- 开发一套针对滚动轴承的可靠故障诊断方法,解决数据稀缺和噪声问题.
- 为了提高在可变条件下轴承故障诊断的准确性和效率.
- 为了利用转移学习,在小样本数据集上进行有效的模型培训.
主要方法:
- 开发了一个自适应的残余收缩网络模型,其中包含了一个适应的参数校正线性单元,用于非线性转换.
- 采用转移学习,在一个大数据集 (Case Western Reserve) 上训练模型,并将其迁移到更小,多样化的数据集 (缩小规模和江南大学).
- 软值和注意力机制被整合在一起,以处理噪音振动信号,高斯白噪声模拟现实世界的噪声.
主要成果:
- 拟议的方法证明了从小样本数据集的有效学习,显著提高了故障诊断的准确性.
- 该模型在可变负载和速度下在诊断轴承故障方面取得了很高的准确性.
- 实验结果证实了算法的强大的抗噪能力和在处理噪音振动信号时的稳定性.
结论:
- 适应性剩余收缩网络与转移学习相结合,为在有限数据的情况下进行滚动轴承故障诊断提供了强大的解决方案.
- 软值和注意力机制的整合增强了该模型处理杂操作数据的能力.
- 这种方法提供了一种可靠和准确的方法,以保持关键机械部件的健康.
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