通过加权Mahalanobis距离方法识别轮故障,该方法基于多尺度顺序变
Xintao Zhou1,2,3, Na Ma4, Jialing Zhang5,4
1School of Mechanical Engineering, Shaanxi Polytechnic University, Xianyang, 712000, China. zxt2006sc@126.com.
Scientific reports
|December 19, 2025
概括
这项研究引入了一种新的轮故障诊断方法,使用优化的多尺度转换 (MPE) 和加权Mahalanobis距离 (MDMaha). 这种方法显著提高了轮故障识别的准确性,在坑和磨损故障中达到99.72%.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 错误诊断 错误诊断 在
背景情况:
- 精确的轮故障检测至关重要,但由于故障信号较弱,因此具有挑战性.
- 现有的方法难以识别轮振动中的微妙异常.
研究的目的:
- 开发一种改进的轮故障诊断方法.
- 为了提高识别轮弱故障信号的准确性.
主要方法:
- 使用相互信息 (MI) 和改进的虚假最近邻居 (IFNN) 来优化多尺度转换 (MPE) 延迟时间 (τ) 和嵌入维度 (m).
- 为故障样本计算的MPE值.
- 适用于初始故障识别的最小Mahalanobis距离 (min-MDMaha).
- 利用信息来根据故障样本特征加权MDMaha.
主要成果:
- 使用min-MDMaha的初始故障识别精度达到了76.87%.
- 加权的MDMaha增强的MPE框架实现了99.72%的显著改进的准确性.
- 该方法有效地描述了轮插孔和磨损故障的振动信号.
结论:
- 拟议的加权MDMaha增强的MPE框架为轮故障诊断提供了一种优越的方法.
- 该方法在表征由轮故障引起的振动信号方面表现出高效率.
- 这种技术提供了一个强大的解决方案,用于准确识别轮中的弱故障信号.
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