排列熵优化改进变模态分解算法诊断齿轮箱故障

TN911.72%TP206; 为了准确提取齿轮箱中复合故障特征,该文选用变模态分解(variational mode decomposition,VMD)对振动信号进行处理,它能够将信号分解为多个固有模态函数(intrinsic mode function,IMF),但需预设分解层数k和惩罚因子;因此,为了能够自适应地确定分解层数k,该文提出了排列熵优化算法(permutation entroy optimization,PEO),该算法可以根据待分解信号的特点自适应的确定分解层数k;同时,为了解决VMD算法对噪声的敏感性,该文根据噪声辅助数据分析的思想,提出了改进VMD算法(modifie...

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Published in农业工程学报 Vol. 34; no. 23; pp. 59 - 66
Main Authors 王志坚, 常雪, 王俊元, 杜文华, 段能全, 党长营
Format Journal Article
LanguageChinese
Published 中北大学机械工程学院,太原,030051%重庆大学机械工程学院,重庆,400044 01.12.2018
Subjects
排列熵
算法
多故障
齿轮
噪声
变模态分解
Online AccessGet full text
ISSN1002-6819
DOI10.11975/j.issn.1002-6819.2018.23.007

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Abstract TN911.72%TP206; 为了准确提取齿轮箱中复合故障特征,该文选用变模态分解(variational mode decomposition,VMD)对振动信号进行处理,它能够将信号分解为多个固有模态函数(intrinsic mode function,IMF),但需预设分解层数k和惩罚因子;因此,为了能够自适应地确定分解层数k,该文提出了排列熵优化算法(permutation entroy optimization,PEO),该算法可以根据待分解信号的特点自适应的确定分解层数k;同时,为了解决VMD算法对噪声的敏感性,该文根据噪声辅助数据分析的思想,提出了改进VMD算法(modified variable modal decomposition,MVMD),该算法首先添加成对符号相反的高斯白噪声到原始信号,再利用VMD算法对其进行分解,经过多次循环,原始信号中的噪声相互抵消,而后将每次循环得到的每层IMF分别进行集成平均.利用该算法分别对含有多故障特征的齿轮箱仿真信号及实测信号进行处理,均提取出了故障特征.该文所提方法对封闭式功率流试验台进行复合故障提取,160和360 Hz的故障频率分别被提取出.该方法为齿轮箱复合故障诊断提供新思路.
AbstractList TN911.72%TP206; 为了准确提取齿轮箱中复合故障特征,该文选用变模态分解(variational mode decomposition,VMD)对振动信号进行处理,它能够将信号分解为多个固有模态函数(intrinsic mode function,IMF),但需预设分解层数k和惩罚因子;因此,为了能够自适应地确定分解层数k,该文提出了排列熵优化算法(permutation entroy optimization,PEO),该算法可以根据待分解信号的特点自适应的确定分解层数k;同时,为了解决VMD算法对噪声的敏感性,该文根据噪声辅助数据分析的思想,提出了改进VMD算法(modified variable modal decomposition,MVMD),该算法首先添加成对符号相反的高斯白噪声到原始信号,再利用VMD算法对其进行分解,经过多次循环,原始信号中的噪声相互抵消,而后将每次循环得到的每层IMF分别进行集成平均.利用该算法分别对含有多故障特征的齿轮箱仿真信号及实测信号进行处理,均提取出了故障特征.该文所提方法对封闭式功率流试验台进行复合故障提取,160和360 Hz的故障频率分别被提取出.该方法为齿轮箱复合故障诊断提供新思路.
Author 常雪
王志坚
杜文华
段能全
党长营
王俊元
AuthorAffiliation 中北大学机械工程学院,太原,030051%重庆大学机械工程学院,重庆,400044
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Duan Nengquan
Dang Changying
Wang Zhijian
Du Wenhua
Chang Xue
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DocumentTitle_FL Gearbox fault diagnosis based on permutation entropy optimized variational mode decomposition
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Keywords 排列熵
算法
多故障
齿轮
噪声
变模态分解
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Title 排列熵优化改进变模态分解算法诊断齿轮箱故障
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