顺序频率的Holo-Hilbert光谱分析用于在时间变化的操作条件下对机器故障诊断
Wanming Ying1, Jinde Zheng2, Wu Huang3
1School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China.
ISA transactions
|February 4, 2024
概括
一种新方法,顺序频率的Holo-Hilbert光谱分析 (OFHHSA),解码时间变化的振动信号用于机械故障诊断. 它通过准确识别故障和抵抗噪声来改进现有技术,即使在变化的操作条件下.
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:
- 全-希尔伯特光谱分析 (HHSA) 有效地将非线性和非静止机械振动中的瞬间特征解调.
- 在不同速度下运行的设备中,HHSA与时间变化的振幅调制 (AM) 特性作斗争.
- 现有的方法可能有局限性,例如定期假设或最大订单截止值.
研究的目的:
- 引入一种新的方法,顺序频率HHSA (OFHHSA),用于从时间变化的振动信号中解调故障信息.
- 用顺序频率的Holo-Hilbert频谱 (OFHHS) 分析时间和角度域之间的相互作用.
- 为了将OFHHSA与基于角度-时间循环-静止框架的顺序频谱相关性 (OFSC) 进行比较,用于机器故障诊断.
主要方法:
- 开发了一种新的角度-时间双层分解结构:顺序频率HHSA (OFHHSA).
- 获得了顺序频率的Holo-Hilbert频谱 (OFHHS) 来描述时间和角度域相互作用.
- 从OFHHS获得了订单AM-边际频谱,以识别故障特征订单.
- 分析了OFHHSA和OFSC之间的差异,用于时间变化的故障诊断.
主要成果:
- 在变速下,OFHHSA成功地从时间变化的振动信号中重新调节故障信息.
- OFHHS光谱图可视化了时间和角度领域之间的相互作用.
- OFHHSA克服了OFSC估计器在定期假设和最大截止顺序方面的局限性.
- 与OFSC相比,模拟和测试的数据表明OFHHSA的精度和防噪能力优越.
结论:
- OFHHSA是一种强大而准确的工具,用于随时间变化的机械故障诊断,特别是在可变的操作条件下.
- 拟议的方法比现有的光谱相关性技术具有显著的优势.
- OFHHSA提供了一种更可靠的方法来识别来自振动信号的设备故障.
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