滚动轴承故障诊断基于同步挤压波形变形和转移残余卷积神经网络
Zihao Zhai1, Liyan Luo1, Yuhan Chen2
1School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
这项研究引入了一种新的故障诊断方法,用于使用同步挤压波波变换 (SWT) 和转移残余卷积神经网络 (TRCNN) 的滚动轴承. 这种技术即使在有限的故障样本上也能达到高准确度,改进了传统方法.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 滚动轴承的故障在机械中至关重要,但非静止信号和有限的数据阻碍了准确的诊断.
- 传统的方法难以从复杂的,时间变化的故障签名中提取特征.
研究的目的:
- 开发一个强大的滚动轴承故障诊断技术,克服特征提取挑战和小样本大小问题.
- 通过改进时间频率表示和先进的神经网络架构来提高诊断准确性.
主要方法:
- 利用同步挤压波段转换 (SWT) 与复杂的莫莱特波段来生成高分辨率的时频图.
- 采用转移残留卷积神经网络 (TRCNN),结合转移学习和分类的残留结构.
- 在TRCNN中输入SWT生成的时间频率图,用于自动故障诊断.
主要成果:
- 拟议的TRCNN在所有12种故障类型中实现了100%的诊断准确性,培训数据有限.
- 与RCNN,TCN,STFT,WT,PWVD和STFA-PD等传统方法相比,明显提高了故障诊断的准确性.
- 证明了三次转移学习在提高诊断性能方面的有效性.
结论:
- 结合SWT和TRCNN的方法为滚动轴承故障诊断提供了强大而准确的解决方案.
- 该方法在处理非静态信号和小样本数据集方面表现出色,这对于现实应用至关重要.
- 这种技术为预测性维护和机械健康监测提供了可靠的基础.
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