使用独立组件分析 (ICA) 方法对滚动元件轴承的基于振动的诊断
Dariusz Mika1, Jerzy Józwik2, Alessandro Ruggiero3
1The Institute of Technical Sciencesand Aviation, The University College of Applied Sciences in Chelm, 22-100 Chełm, Poland.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
独立组件分析 (ICA) 通过分离振动信号,有效检测轴承故障. 这种方法提高了在旋转机械诊断中检测故障信号的性能.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
背景情况:
- 滚动元件轴承在旋转机械中至关重要.
- 轴承缺陷会在振动信号中产生特有的故障频率和侧带.
- 准确的故障诊断需要将这些组件从重叠的来源中分离出来.
研究的目的:
- 应用盲源分离 (BSS),特别是独立组件分析 (ICA),用于检测滚动元件轴承的局部故障.
- 调查ICA对诊断指标和故障组件检测能力的影响.
主要方法:
- 在模拟旋转机械设置的振动信号上使用线性ICA算法.
- 模拟了用于数据采集的常见轴承故障条件.
- 分析了ICA信号分解对统计诊断指标的影响.
主要成果:
- ICA显著改善了对振动源的分离.
- 提高了分析信号中的故障标记的清晰度.
- 证明了与故障相关的组件的可检测性得到改善.
结论:
- 盲源分离,特别是ICA,对于基于振动的诊断是有效的.
- ICA提高了轴承故障检测系统的准确性和稳定性.
- 国际机械协会 (ICA) 显示出作为旋转机械健康监测的补充工具的潜力.
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