基于振动的变速箱故障诊断使用多尺度卷积神经网络,具有深度智能的特征连接
Van-Trang Nguyen1, Quoc Bao Diep2
1Faculty of Vehicle and Energy Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City, Vietnam.
PloS one
|July 7, 2025
概括
一个新的MixNet深度学习模型使用振动数据准确地诊断变速箱故障. 这种非侵入性方法提高了工业设备的可靠性,并使得早期检测到故障.
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:
- 变速箱故障在工业环境中至关重要,导致停机时间和安全风险.
- 准确和及时的故障诊断对于运营效率和降低成本至关重要.
- 现有的方法可能缺乏稳定性或需要侵入性监测.
研究的目的:
- 为基于振动的变速箱故障诊断提出一种新的深度学习方法.
- 提高工业环境中的诊断准确性和稳定性.
- 为了实现非侵入性,自动化和早期故障检测.
主要方法:
- 使用了一个多尺度卷积神经网络,具有深度智能特征连接 (MixNet).
- 使用短时间里埃变换 (STFT) 来从振动信号中生成光谱图.
- 使用多尺度卷积层和特征连接提取的区分特征.
主要成果:
- 在变速箱故障诊断数据集上,MixNet实现了99.32%的诊断准确率.
- 与现有的深度学习模型相比,该模型表现出了优异的性能.
- 训练时间高效为4分29秒.
结论:
- 拟议的MixNet模型为变速箱故障诊断提供了一个高度准确和计算高效的解决方案.
- 这种非侵入性,自动化的方法支持基于条件的维护,并降低成本.
- 混合网络适用于制造,能源和汽车应用中的实时监控.
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