一种新的智能故障诊断方法用于基于多维关注的变速箱,拒绝卷积
Wei Liu1, Zeqiao Zhang1, Zhiwei Ye1
1School of Computer Science, Hubei University of Technology, Wuhan, 430068, China.
Scientific reports
|October 21, 2024
概括
一个新的多维融合残留注意网络 (MFRANet) 通过抑制噪音和从工业振动数据中提取多尺度特征,有效地诊断变速箱故障. 这种深度学习方法提高了旋转机械故障诊断的可靠性和准确性.
科学领域:
- 人工智能的人工智能
- 机械工程 机械工程
- 信号处理 信号处理
背景情况:
- 卷积神经网络 (CNN) 在工业旋转机械故障诊断中与杂的多尺度振动数据作斗争.
- 有效的噪声抑制和多尺度特征提取对于准确的智能故障诊断至关重要.
- 现有的方法在处理现实世界的工业振动信号的复杂性方面面临挑战.
研究的目的:
- 提出一种新的深度神经网络框架,即多维融合残留注意网络 (MFRANet),用于变速箱故障诊断.
- 解决CNN在处理噪音和多尺度工业振动数据方面的局限性.
- 为了提高旋转机械故障诊断的准确性和稳定性.
主要方法:
- 开发了多维融合残留注意网络 (MFRANet),采用了多尺度深可分离卷积模块.
- 集成了一个剩余通道注意模块,用于特征地图无声化和权重.
- 采用外部注意模块来捕捉在多尺度特征中隐含的相关性.
主要成果:
- 与基准和最先进的方法相比,MFRANet在变速箱故障数据集上表现出优异的诊断性能.
- 拟议的方法在各种噪声级别中表现出强大的抗噪能力,表明提高了可靠性.
- 实验评估证实了MFRANet在准确提取多尺度断层特征方面的有效性.
结论:
- MFRANet为旋转机械的变速箱故障诊断提供了一个创新的,高效的解决方案.
- 该研究强调了先进的深度学习架构在解决实际工业挑战方面的潜力.
- 拟议的框架为要求苛刻的工业环境中智能故障诊断提供了一种可靠的方法.
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