一种新的轮轴承故障诊断方法
Runtao Sun1,2, Jianwei Yang1,2,3, Dechen Yao1,2,3
1School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
这项研究引入了一种适应技术,用于诊断列车中的滚动轴承故障,即使在噪音条件下. 该方法准确地识别了复合轴承的故障,改善了铁路安全和维护.
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:
- 铁路列车运行面临着恶劣的条件,如变速和重载.
- 滚动轴承的故障带来了重大的安全和运行风险.
- 现有的诊断技术在噪音和特征提取准确性方面扎.
研究的目的:
- 开发一种适应性技术,用于准确识别铁路车辆的滚动轴承缺陷.
- 在杂的环境中克服传统方法的局限性.
- 为了增强故障特征提取,提高诊断可靠性.
主要方法:
- 集成多点最佳最小解卷调整 (MOMEDA) 的信号过和冲击组件增强.
- 拉马努贾子空间分解的应用用于自动信号组件分解.
- 开发一个可适应的模块,以解决传统方法中的冗余性和不准确性.
主要成果:
- 这种新技术精确地提取复合材料轴承的缺陷,即使有显著的噪声干扰,被信封频谱分析证实.
- 使用信号噪声比 (SNR) 和故障缺陷指数进行定量评估,证明了有效的无声化和强大的故障提取.
- 该方法在模拟和实验中与广泛使用的信号分解技术相比,显示出更高的性能.
结论:
- 拟议的自适应技术有效地在具有挑战性的运行条件下识别火车轮组的轴承故障.
- 组合的MOMEDA和拉马努贾子空间分解方法为噪音振动信号提供了强大的解决方案.
- 这种方法提高了铁路行业滚动轴承诊断的可靠性和准确性.
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