基于深度残余神经网络算法的变速箱故障检测方法的优化
Zhaohua Wang1, Yingxue Tao1, Yanping Du1
1Department of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, No. 1, Xinghua Street, Beijing 102600, China.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
轮故障诊断对于机械健康至关重要. 这项研究引入了一种改进的ResNeXt50模型与CBAM,用于更准确,更快速地检测轮故障,优于其他方法.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 由于运营需求,轮故障很常见,使得早期检测具有挑战性.
- 对于轮故障诊断的传统神经网络模型存在复杂性和长时间的训练时间问题.
研究的目的:
- 开发一种高效准确的轮故障检测方法.
- 增强功能提取能力,以提高诊断性能.
主要方法:
- 在ResNeXt50网络中集成了卷积块注意模块 (CBAM).
- 将1D振动信号转换为2D图像,使用最佳时间频率分析来降低噪音.
- 在变速箱故障数据集上训练和测试模型,并将其与经典的卷积神经网络模型进行比较.
主要成果:
- 拟议的CBAM-ResNeXt50模型显示出卓越的故障识别准确性.
- 与其他模型相比,该模型实现了显著减少的平均训练时间.
- 在两个不同的工作条件下观察到有效的绩效.
结论:
- CBAM-ResNeXt50方法为变速箱故障诊断提供了一个有效的解决方案.
- 这种方法提高了检测轮故障的准确性和效率.
- 这些发现为当前轮故障诊断研究提供了宝贵的见解.
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