使用基于多尺度形态学和改进的PSO算法的过算法进行轴承故障检测的研究
Peng Wang1, Huizhen Zhao1, Naijiang Liu1
1College of Mechanical Engineering, North China University of Science and Technology, Tangshan, China.
The Review of scientific instruments
|December 9, 2025
概括
本研究介绍了一种先进的多尺度数学形态选方法,用于改进轴承故障检测. 改进的技术在噪音条件下准确识别故障,优于传统方法.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 振动分析 振动分析
背景情况:
- 数学形态过是常见的轴承故障检测,但与噪声和短暂的特征斗争.
- 现有的方法在复杂的噪音环境中缺乏稳定性.
研究的目的:
- 开发一个增强的多尺度数学形态过架构,以改善轴承故障检测.
- 为了解决噪声灵敏度和短暂特征提取方面的局限性.
主要方法:
- 提出了使用数学形态学原理的增强过架构.
- 运营商选择所使用的特征频率强度系数.
- 开发了一种通过粒子群优化和帐混乱序列优化的多尺度运算符.
- 通过模拟和实验性故障轴承数据验证了战略.
主要成果:
- 拟议的多尺度过策略有效地捕获噪音振动信号中的弱周期性短暂组件.
- 在检测内环,外环和滚动元件故障方面取得了卓越的准确性.
- 与传统方法相比,在复杂的噪音环境中表现得更好.
结论:
- 增强的多尺度数学形态过策略为轴承故障检测提供了强大的解决方案.
- 该方法在振动信号分析中显著提高了准确性和抗噪能力.
- 这种方法为旋转机械的状态监测提供了有价值的工具.
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