铁路车轴承故障诊断方法基于改进的子搜索算法,优化变化模式分解和多级卷积神经网络
Zhihui Men1, Zhe Chen1, Yonghua Li1
1College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China.
The Review of scientific instruments
|April 1, 2024
概括
本研究引入了一种改进的子搜索算法,用于精确监测铁路车轴承状况. 改进的方法显著提高了诊断准确度和故障检测效率,以提高火车运行安全性.
科学领域:
- 铁路工程 铁路工程是指铁路工程.
- 机械故障诊断 机械故障诊断
- 计算智能是一种计算智能.
背景情况:
- 精确的轴承状况监测对于安全的列车运行至关重要.
- 铁路车轴承的传统维护方法缺乏实时精度和效率.
- 机械设备诊断需要先进的,数据驱动的方法.
研究的目的:
- 通过物流混乱映射和征收飞行策略来增强子搜索算法 (SSA).
- 优化变化模式分解 (VMD) 参数,以改善轴承故障诊断.
- 为铁路车轴承开发一个强大的故障诊断方法.
主要方法:
- 将物流混乱映射和征收飞行纳入SSA.
- 优化VMD参数使用内在模式组件的平均分散作为适应性函数.
- 将优化的VMD与多级卷积神经网络 (CNN) 集成,用于故障诊断.
主要成果:
- 增强的SSA显示出更好的空间搜索能力和减少的 modal aliasing.
- 与传统模型 (LeNet-5,AlexNet) 相比,多层次的CNN实现了更高的诊断准确度和更快的融合速度.
- 在公共数据集和专用平台上的实验验证证证了该方法的有效性.
结论:
- 拟议的方法为铁路车轴承故障诊断提供了高度准确和高效的解决方案.
- 这种方法符合"智能"时代对机械设备智能监控的要求.
- 这项研究为提高铁路运输的安全性和可靠性提供了宝贵的工具.
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