频段相关性因子 (BRF):一种基于旋转机械振动分析的新型自动频段选择方法
Lucas Costa Brito1, Gian Antonio Susto2, Jorge Nei Brito3
1School of Mechanical Engineering, Federal University of Uberlândia, Av. João N. Ávila, 2121, Uberlândia, Brazil.
ISA transactions
|October 4, 2024
概括
本研究介绍了带相关性因子 (BRF),这是一种用于识别旋转机械振动信号中关键频段的自动方法. BRF通过精确确定与操作变化或故障相关的所有相关频段来增强机器健康监测.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 状态监控 状态监控
背景情况:
- 有效监控旋转机械对于工业生产至关重要.
- 从振动信号中提取相关信息是状态监测的关键.
- 现有的频段选择方法往往错过了关键信息,需要手动调节参数.
研究的目的:
- 提出一种自动方法来选择振动信号中的所有相关频段.
- 克服现有方法的局限性,这些方法只关注冲动激发或需要手动调整.
- 识别频段,表明机器动态的变化或发展故障.
主要方法:
- 开发了一种名为带相关性因子 (BRF) 的新方法.
- BRF使用光谱来自动识别相关频段.
- 结果通过相关性排名和热图可视化呈现.
主要成果:
- BRF成功地执行了相关频段的自动选择.
- 该方法识别了与机器行为变化和故障相关的频段.
- 在合成和现实数据集上的验证证实了BRF的有效性.
结论:
- BRF为旋转机械中的振动信号分析提供了有效的自动解决方案.
- 该方法提高了检测故障和监控机器健康的能力.
- 通过识别所有相关的光谱区域,BRF提供了对机器状况的宝贵见解.
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