基于改进的维纳波器过的滚动轴承降噪方法
The Review of scientific instruments
|February 14, 2025
概括
本研究介绍了一种适应性降噪技术,用于轴承故障诊断. 该方法通过自适应地确定Wiener过参数,有效地识别复合故障,提高精度.
科学领域:
- 机械工程 机械工程
- 振动分析 振动分析
- 错误诊断 错误诊断 错误诊断 错误诊断 错误诊断 错误诊断
背景情况:
- 准确识别复合轴承故障对于机械维护至关重要.
- 传统的降噪方法与振动信号中复杂的故障特征作斗争.
- 功能模式分解 (FMD) 为信号分离提供了一个潜在的途径.
研究的目的:
- 为准确的复合轴承故障识别开发一种新的降噪方法.
- 根据信号特征,以适应性来确定维纳过参数.
- 为了提高旋转机械故障诊断的可靠性.
主要方法:
- 振动信号通过特征模式分解 (FMD) 将其分解为模式组件.
- 信号评估指数 (kurtosis,RMS,变量) 和欧几里德距离被用来选择Wiener过的最佳信号.
- 维纳过的顺序是通过适应性决定的,使用最大的Kurtosis作为标准.
主要成果:
- 拟议的方法通过自适应性来确定输入信号和Wiener过顺序.
- 它有效地将与故障相关的频率组件与噪声分开.
- 与经典方法的比较显示出优越的噪声限制和更准确的复合故障识别.
结论:
- 提出的适应性降噪方法显著提高了复合轴承故障诊断的准确性.
- 该技术为分析机械中复杂的振动信号提供了强大的解决方案.
- 这种方法增强了用于承载健康监测的诊断能力.
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