基于多模式方法的轴承故障诊断研究
Hao Chen1,2, Shengjie Li1, Xi Lu1,2
1School of Information Engineering, Nantong Institute of Technology, Nantong 226002, Jiangsu, China.
Mathematical biosciences and engineering : MBE
|January 14, 2025
概括
这项研究引入了一种新的多尺度时间频率和统计特征融合模型 (MTSF-FM),用于准确的轴承故障诊断. 该模型有效地分析复杂的振动数据,在识别轴承缺陷方面达到高精度.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 轴承故障诊断对于机械系统安全至关重要.
- 轴承的振动数据往往呈现出复杂的非静止和非线性特征.
- 现有的方法难以应对轴承振动信号的复杂性质.
研究的目的:
- 开发一种用于增强轴承故障诊断的新型模型.
- 为应对非静止和非线性振动数据所带来的挑战.
- 提高轴承状况监测的准确性和可靠性.
主要方法:
- 开发了一种多尺度的时间频率和统计特征融合模型 (MTSF-FM).
- 连续波段转换生成的时间频率图像用于特征提取.
- 使用视觉几何组和卷积神经网络从图像和时间频率数据中进行深度特征提取.
主要成果:
- 在两个公共数据集上,MTSF-FM模型实现了98.5%和95.1%的高诊断准确率.
- 多尺度时间频率和统计特征的融合被证明是有效的.
- 该模型成功捕获了复杂的信号特征,以改进故障检测.
结论:
- MTSF-FM模型为轴承故障诊断提供了一种强大而有效的方法.
- 这种方法在预测性维护中展示了现实世界应用的巨大潜力.
- 该研究强调了结合多种特征提取技术来进行复杂信号分析的价值.
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