一维时频双通道视觉变压器,用于在强噪声和有限数据条件下进行轴承故障诊断
Shaobin Cai1,2, Yuchen Wang3, Wanchen Cai4
1College of Information Engineering, Huzhou University, Huzhou, 313000, China.
Scientific reports
|July 20, 2025
概括
这项研究引入了一种先进的视觉变压器模型,用于稳固的轴承故障诊断,即使数据有限和噪声水平高,也显著提高了准确性. 这种新的方法提高了工业环境中的运行安全.
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:
- 轴承健康对于工业设备的稳定性和运行安全至关重要.
- 变压器模型在轴承故障诊断方面表现有前途,但在噪音和有限数据方面存在困难.
- 准确而高效的故障诊断对于防止设备故障至关重要.
研究的目的:
- 为增强轴承故障诊断提出一个端到端视觉变压器模型.
- 解决轴承故障检测中噪音和数据稀缺的挑战.
- 提高工业环境中诊断系统的稳定性和准确性.
主要方法:
- 开发了一个端到端的视觉变压器模型,集成时间频率融合和双重注意力 (空间和通道).
- 采用双分支设计:使用注意力机制的时间域分支和使用快速里埃转换 (FFT) 的频率域分支.
- 集成的交叉尺度卷积和定期的前网络来增强特征提取.
主要成果:
- 在CWRU数据集上达到99.42%的准确性,在PU数据集上达到98.14%的准确性.
- 与最先进的方法相比,在噪音和数据稀缺条件下表现出卓越的性能.
- 证实了显著的噪声稳定性和诊断准确性.
结论:
- 拟议的视觉变压器模型为工业轴承故障诊断提供了实用和有效的解决方案.
- 时频融合和双重注意力机制提高了诊断能力.
- 该模型显示了对需要可靠设备监控的现实应用的巨大潜力.
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