基于人类耳朵模型的共振峰提取方法及其在轴承故障诊断中的应用
Yongming Zhao1, Yungong Li2, Chao Wang1
1School of Mechanical Engineering and Automation, Northeastern University, Shenyang, 110089, Liaoning, China.
Scientific reports
|November 25, 2025
概括
这项研究引入了一种新的共振峰值提取方法 (RESAS),用于改进滚动轴承故障检测. 该方法通过模拟听觉注意力来提取特征来提高诊断准确性.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 机器状态监测 机器状态监测
背景情况:
- 滚动轴承是旋转机械中的重要部件.
- 准确的轴承故障检测对于操作可靠性至关重要.
- 现有的共振峰提取方法面临着低质量和假峰等挑战.
研究的目的:
- 开发一种新且强大的共振峰提取方法.
- 为了提高滚动轴承故障分类的准确性.
- 为了解决现有的共振峰提取技术的局限性.
主要方法:
- 开发了一种基于听觉突出度 (RESAS) 的新共振峰值提取方法.
- RESAS使用了Gammatone过,多尺度高斯过和横向抑制.
- 提取的特征被输入到一个改进的随机森林模型 (TF-RF) 中,用于故障分类.
主要成果:
- 该RESAS方法有效地提取共振峰值,克服了低质量和虚假峰值的问题.
- TF-RF模型在各种速度和负载中实现了准确的轴承故障分类.
- 该方法显示了在现实场景中应用的强大潜力.
结论:
- 拟议的RESAS方法显著提高了滚动轴承故障检测.
- 该方法显示了可扩展性到其他冲击类型故障系统的承诺.
- 这种技术为旋转机械的状态监控提供了强大的解决方案.
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