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基于AUPLMD和RTSMWPE的故障诊断方法用于反转式压缩机

Meiping Song¹, Jindong Wang¹, Haiyang Zhao¹

¹Mechanical Science and Engineering Institute, Northeast Petroleum University, Daqing 163318, China.

Entropy (Basel, Switzerland)

|July 8, 2023

概括

这项研究引入了一种用于振动信号的新型故障特征提取方法,它结合了自适应均相局部平均分解 (AUPLMD) 和精细的时移多尺度加权变量 (RTSMWPE) 以提高准确性.

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错误诊断错误诊断错误诊断

背景情况:

振动信号分析对于机械故障诊断至关重要.
像局部平均分解 (LMD) 这样的传统方法遭受 modal aliasing 的影响.
变 (PE) 对时间序列的长度敏感,限制了它的应用.

研究的目的:

为振动信号开发一种先进的故障特征提取方法.
为了解决LMD的 modal aliasing问题.
为了克服PE的时间序列长度依赖性.

主要方法:

具有自适应的正弦波掩盖和基于kurtosis的重建的自适应均相位局部平均分解 (AUPLMD).
精细的时移多尺度加权变换 (RTSMWPE) 包含振幅信息和时移多尺度分析.
适用于来自反转压缩机门的实验数据.

主要成果:

通过自适应信号处理,AUPLMD有效地抑制模态别名并消除噪声.
RTSMWPE通过考虑振幅和使用时移方法来增强故障特征提取.
组合方法在分析反复压缩机实验数据方面表现出卓越的性能.

结论:

拟议的AUPLMD-RTSMWPE方法为振动信号中的故障特征提取提供了一个强大的解决方案.
这种方法显著改进了现有的LMD和PE技术.
该方法在现实世界互换压缩机故障诊断中的有效性得到了验证.

关键词:

适应性均相位局部平均值分解错误诊断错误诊断错误诊断是一个问题.功能提取特性提取交换式压缩机门的交换式压缩机精细的时间转移多尺度加权变换热量.

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