基于改进复合多尺度样本熵的行星齿轮箱故障诊断
Fault diagnosis of planetary gearbox based on improved composite multi-scale sample entropy
李伟 1王付广 2王东生1
作者信息
- 1. 铜陵学院机械工程学院,安徽铜陵 244000;铜陵学院安徽铜基新材料产业共性技术研究中心,安徽铜陵 244000
- 2. 铜陵学院机械工程学院,安徽铜陵 244000
- 折叠
摘要
针对多尺度样本熵受样本长度影响较大,且粗粒化过程较粗糙,易忽略有效信息的不足,在复合多尺度样本熵的基础上,以采样点间能量分布作为权重进行粗粒化计算,提出了改进的复合多尺度样本熵,并将其应用于行星齿轮箱故障诊断.通过仿真信号研究不同参数和不同噪声特性对改进复合多尺度样本熵算法的影响,将其与多尺度样本熵、广义多尺度样本熵、复合多尺度样本熵进行对比,验证了本文改进算法的稳定性.结合变分模态分解、主成分分析和支持向量机对行星齿轮箱实验信号进行故障诊断.对比结果表明:所提方法能够有效地实现不同工况和不同结构行星齿轮箱太阳轮常见故障诊断,且故障识别率达到95％以上,具有一定的有效性.
Abstract
In view of the fact that the multi-scale sample entropy is greatly affected by the sample length,the coarse graining process is relatively rough,and the shortage of effective information may be easily ignored,based on the composite multi-scale sample entropy,the energy distribution between sampling points was used as the weight for coarse graining calculation,and an improved composite multi-scale sample entropy was proposed and applied to the fault diagnosis of planetary gearbox.The influences of different parameters and noise characteristics on the improved composite multi-scale sample entropy algorithm were studied through simulation signals.The stability of the improved algorithm was verified by comparing it with multi-scale sample entropy,generalized multi-scale sample entropy and composite multi-scale sample entropy.Combined with variational mode decomposition,principal component analysis and support vector machine,the fault diagnosis of planetary gearbox experimental signals was carried out.The comparison results showed that the method can effectively realize the common fault diagnosis of the sun gear of the planetary gearbox under different working conditions and structures,and the fault identification rate was more than 95％,with certain effectiveness.
关键词
多尺度样本熵/变分模态分解/支持向量机/行星齿轮箱/故障诊断Key words
multiscale sample entropy/variational modal decomposition/support vector machine/planetary gearbox/fault diagnosis引用本文复制引用
基金项目
国家自然科学基金(51205198)
安徽省自然科学基金(2008085ME149)
安徽省高校科研基金(2023AH051665)
平台建设协同创新项目(GXXT-2022-090)
校级自然科研项目(2022tlxy49)
出版年
2024
NETL
NSTL
万方数据