在强烈的车轮轨道冲击干扰下,地铁列车轴承的初始故障诊断使用改进的互补CELMDAN和基于混合电流的自适应功能增强
Jun Chen1, Chunrong Hua2, Dawei Dong2
1Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, Hunan, China.
ISA transactions
|February 6, 2024
概括
一种新方法可以准确地诊断地铁列车轴承故障,尽管有噪音和干扰. 这种方法结合了改进的信号分解和适应性功能增强,以实现可靠的早期故障检测.
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:
- 在地铁列车轴承中诊断初始故障是具有挑战性的,原因是显著的车轮轨道冲击干扰和背景噪音.
- 现有的方法在这些恶劣条件下难以提取可靠的故障特征.
研究的目的:
- 提出一种新的特征提取方法,用于准确诊断初始地铁列车轴承故障.
- 为了提高通过强干扰掩盖的弱故障信号的检测.
主要方法:
- 引入了改进的互补完整局部平均分解与适应性噪声 (ICCELMDAN) 进行强大的特征提取.
- 采用基于混合电流的自适应特征增强 (AFE),使用海洋捕食者算法 (MPA) 来优化参数和增强故障信号.
- 结合了ICCELMDAN的特征提取与AFE的增强能力.
主要成果:
- 提出的ICCELMDAN-AFE方法成功地从故障轴承信号中提取了完整的特征,即使在强大的背景噪声下.
- 较弱的故障特征信号得到了有效的增强,克服了强大的车轮-轨道冲击干扰.
- 在模拟和实际场景中,都能准确诊断弱型地铁列车轴承故障.
结论:
- 与现有技术相比,集成的ICCELMDAN-AFE方法在特征提取完整性,诊断准确性和稳定性方面表现出卓越的性能.
- 这种方法为关键地铁列车组件的早期故障检测提供了可靠的解决方案.
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