基于GMNR和ResNet-CABA-MAGRU的轴承故障诊断研究
Longfa Chen1, Na Meng1, Wenzheng Sun1
1School of Intelligent Equipment, Shandong University of Science and Technology, Tai'an 271019, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
一种新的轴承故障诊断模型 (GMNR-CABA-MAGRU) 实现了高准确性和速度,即使在噪音条件下. 这种强大的模型在故障诊断方面表现出色,在各种测试中表现优于其他模型.
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:
- 滚动轴承故障诊断在杂的环境中面临着准确性,速度和概括性的挑战.
- 现有的方法在强噪声条件下难以保持性能.
研究的目的:
- 开发一个强大的轴承故障诊断模型 (GMNR-CABA-MAGRU),克服噪音环境中的局限性.
- 为了提高诊断准确度,减少运行时间,提高概括能力.
主要方法:
- 提出了一种新型模型,将格拉姆消噪模块 (GMNR) 与注意力机制改进的残余网络 (ResNet-CABA) 集成在一起.
- 使用CWRU轴承数据集在各种噪音水平和负载条件下验证模型.
主要成果:
- 在无噪声环境中实现了99.66%的准确性和52.74s的运行时间.
- 在强噪声环境 (-4db SNR) 中保持了90.32%的准确性.
- 与现有模型相比,在不同负载条件下表现出卓越的性能和通用性.
结论:
- 该GMNR-CABA-MAGRU模型为滚动轴承故障诊断提供了卓越的诊断精度和稳定性.
- 开发的框架有效地处理强噪声环境,并表现出增强的概括能力.
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