一种基于快速短图和解调和零碎总和近似的同步压缩转换方法,用于轴承故障诊断
Yanlu Chen1, Lei Hu2, Niaoqing Hu3
1College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
本研究引入了一种改进的同步压缩转换 (SST) 方法,用于识别变速滚动轴承故障. 改进的技术有效地从复杂的信号中提取故障特征的频率.
科学领域:
- 工程 工程师 工程师 工程师
- 信号处理 信号处理
- 机械振动 - 机械振动
背景情况:
- 同步压缩变换 (SST) 是一种有价值的时间频率分析工具,在各种领域都有应用.
- 传统的SST方法在时间频率分辨率和处理长,时间变化的信号方面存在局限性,特别是在故障诊断方面.
研究的目的:
- 为了增强同步压缩变换 (SST) 以在变速滚动轴承故障诊断中准确地提取特征频率.
- 在时间频率分辨率和信号长度处理方面克服传统SST的局限性.
主要方法:
- 这是一种新的方法,它结合了快速的 kurtogram,基于希尔伯特变换的解调,以及用于信号预处理的逐步聚合近似 (PAA).
- 快速的 kurtogram 和希尔伯特变换被用来过和解调信号,减少噪声和提高分辨率.
- 用零碎总和近似 (PAA) 来压缩长信号,从而实现高效的SST应用.
主要成果:
- 拟议的方法在识别变速滚动轴承故障的特征频率方面表现出卓越的性能.
- 实验数据验证证实了增强的SST在故障检测方面的有效性.
- 综合方法在具有挑战性的信号条件下显著提高了故障诊断的准确性和效率.
结论:
- 开发的同步压缩转换方法,集成快速 kurtogram,解调和PAA,为变速滚动轴承故障诊断提供了强大的解决方案.
- 这种增强技术有效地解决了传统SST的局限性,提供了准确的故障特征频率提取.
- 这些发现表明,在旋转机械的状态监测和预测性维护方面取得了有前途的进展.
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