基于时空特征融合的不同操作条件的轴承故障诊断方法的研究
Jin Wang1, Yan Wang1,2, Junhui Yu1,2
1School of Electronic Information, Sichuan University, Chengdu 610065, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
这项研究引入了一种新的时空特征融合域自适应网络 (STFDAN),用于在可变操作条件下准确的轴承故障诊断. 该框架有效地解决了使用先进的特征提取和适应技术的领域转移问题.
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:
- 轴承旋转速度的变化导致振动数据特征不一致,阻碍了跨操作条件的直接模型应用.
- 目标域中的有限的标记数据在不断变化的操作参数下使故障诊断复杂化.
研究的目的:
- 提出一个时空特征融合域自适应网络 (STFDAN),用于在不同操作条件下进行可靠的轴承故障诊断.
- 开发一个能够处理领域转移和轴承故障诊断中的数据稀缺性的框架.
主要方法:
- 利用快速里叶变换 (FFT) 和变化模式分解 (VMD) 进行多层次的光谱和模态特征提取.
- 使用并行卷积神经网络 (SECNN) 和双向长期短期记忆 (BiLSTM) 网络进行时空特征提取.
- 综合交叉注意功能融合和多核最大平均差异 (MKMMD) 域分布对齐.
主要成果:
- 在使用基准数据集 (CWRU,JNU,SEU) 的不同负载条件下,STFDAN框架表现出高的诊断准确性.
- 实现了有效的故障诊断,尽管由于不同的操作条件造成的域移动.
结论:
- 拟议的STFDAN框架成功地解决了在不同的操作条件下进行轴承故障诊断的挑战.
- 该方法为需要可靠的轴承状况监测的现实应用提供了有前途的解决方案.
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