基于MSC-SGMD的火车轴箱轴承故障诊断
Yongliang Bai1, Hai Xue1, Jiangtao Chen1
1School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
本研究引入了一种新方法,用于检测火车轴箱轴承的复杂故障. 该技术增强了从振动信号中提取故障特征,提高了火车的维护和性能.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 状态监控 状态监控
背景情况:
- 列车轴箱轴承在恶劣条件下运行,使复合故障诊断具有挑战性.
- 这些轴承的有效维护对于确保高质量的列车性能和运行安全至关重要.
研究的目的:
- 提出一种新的信号重建算法,用于改进列车轴箱轴承中复合故障特征的提取.
- 为了提高在不同操作速度下轴承故障的诊断.
主要方法:
- 这项研究利用Symplectic Geometry Mode Decomposition (SGMD) 将振动信号分解为Symplectic Geometry Components (SGCs) 的方法进行研究.
- 使用最大光谱连贯性标准来识别和重建关键信号组件.
- 封面频谱分析用于从重建的信号中提取故障特征.
主要成果:
- 拟议的最大光谱连贯度信号重建算法有效地从轴箱轴承振动信号中提取复合故障特征.
- 该方法在不同速度条件下识别轴承故障方面表现得更好.
- 在轴承故障模拟试验台上的实验验证证证了算法的有效性.
结论:
- 开发的算法在诊断火车轴箱轴承的复合故障方面取得了重大进展.
- 这种方法为铁路系统的状态监测和预测性维护提供了更可靠的方法.
- 该技术处理不同速度的能力使其成为现实世界的应用中宝贵的工具.
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