风力轮机变速箱故障诊断方法的研究,使用单数值分解和图形里埃变换
Lan Chen1, Xiangfeng Zhang1, Zhanxiang Li1
1College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumchi 830017, China.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
这项研究引入了一种新的变速箱故障诊断方法,使用单数值分解 (SVD) 和图形里埃变换 (GFT). 该技术可以准确地识别变速箱故障,即使在杂的环境中也是如此.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 错误诊断 错误诊断 错误诊断 是一个
背景情况:
- 变速箱在恶劣条件下容易发生故障.
- 环境噪音严重阻碍了机械设备的准确故障诊断.
- 现有的方法往往忽略了单数值之间的相互关系,失去了关键的故障信息.
研究的目的:
- 开发一种强大的故障诊断方法,用于抗环境噪声的变速箱.
- 以有效地从轮振动信号中提取微妙的故障信息.
- 提高机械故障诊断的准确性和可靠性.
主要方法:
- 从振动信号中提取特征的单值分解 (SVD).
- 图形信号处理 (GSP) 从单数值构建图形信号,捕获拓关系.
- 图表里埃变换 (GFT) 用于提取光谱域中的断裂特征.
- 马哈拉诺比斯距离用于辨别和诊断不同的缺陷状态.
主要成果:
- 拟议的方法通过分析单数值之间的相互关系,有效地捕获微妙的故障信息.
- 图形信号处理和GFT能够在光谱领域进行强大的特征提取.
- 对轴承和轮故障的实验验证证证了增强的噪声强度.
- 即使在噪音较高的环境中,也可以实现变速箱故障的准确诊断.
结论:
- 集成SVD,GSP和GFT提供了一种强大的方法来诊断变速箱故障.
- 与传统技术相比,该方法在噪音条件下表现优越.
- 这种方法提高了关键机械系统故障诊断的可靠性.
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