基于改进的MUSIC算法,对大型离心压缩机的刀片异步振动参数识别进行研究
Zhenfang Fan1, Yongtao Shen1, Yupeng Du1
1School of Mechanical Engineering, North University of China, Taiyuan 030051, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
离心压缩机的叶片尖端计时 (BTT) 现在可以更好地监测叶片的健康状况. 改进的MUSIC算法准确识别了刀片异步振动参数,大大减少了错误.
科学领域:
- 机械工程 机械工程
- 振动分析 振动分析
- 状态监控 状态监控
背景情况:
- 叶片尖端定时 (BTT) 对于分析大型离心压缩机叶片振动至关重要.
- 准确识别刀片异步振动参数是有效的BTT状态监测的关键.
研究的目的:
- 将MUSIC和ESPRIT算法用于识别异步振动参数进行比较.
- 提出一个改进的MUSIC算法,以提高BTT分析的准确性.
主要方法:
- 应用多重信号分类 (MUSIC) 和通过旋转不变技术 (ESPRIT) 算法估计信号参数.
- 根据ESPRIT的结果引入了"频率分布率"概念.
- 开发了一种改进的MUSIC算法,通过将结果与频率分布率进行加权.
主要成果:
- 改进的MUSIC算法显示出高准确度,与应变计相比,最大相对误差为0.23%.
- 拟议的方法提高了在MUSIC算法输出中识别真实频率的信心.
- 该研究讨论了MUSIC和ESPRIT算法的优缺点.
结论:
- 改进的MUSIC算法显著提高了刀片异步振动参数识别的准确性.
- 这一进步对于BTT技术在离心压缩机中的工业应用至关重要.
- 这些发现有助于更可靠地监测关键旋转机械的状态.
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