基于EEMD降噪和滑动峰态解调的滚动轴承故障诊断方法
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摘要
针对滚动轴承早期故障特征十分微弱且易被强背景噪声淹没这一问题,引入零时滞4阶累积量(即峰态因数)用于描述信号中弱冲击成分偏离高斯分布的程度,从而提出一种基于EEMD降噪和滑动峰态解调的滚动轴承早期故障诊断方法。该方法首先将故障信号进行EEMD分解得到一组IMFs,根据度量因子和信息熵准则筛选出最能表征故障信息的IMF分量进行重构;然后,利用谱峭度方法自动确定带通滤波器参数进行带通滤波,对滤波后的信号采用滑动峰态算法计算峰态时间序列即峰态包络,并根据频谱分析结果诊断轴承故障发生部位。该方法解决了带通滤波器参数选择问题,同时可提取轴承早期故障振动信号中的微弱冲击成分。利用试验信号对新方法进行检验。结果表明:与传统方法相比,新方法能够有效解决强噪声背景下的早期微弱故障的特征提取问题。
In order to solving the problem that the early fault features of rolling bearing are very weak and easy to be destroyed by strong background noise,the zero delay forth-order cumulant(i.e.kurtosis factor) is introduced for describing the degree of weak impacts deviating from Gauss distribution.A new method for incipient fault diagnosis of rolling element bearings is proposed based on EEMD Denoising and Sliding Kurtosis Demodulation(SKD).Firstly,fault signals were decomposed into a group of IMFs with EEMD;Secondly,the IMF best representing the fault information was selected to reconstruct signals using an adaptive optimization algorithm which constructed by information entropy and measure coefficient.By virtue of the spectral kurtosis analysis of the new composite signal.the optimal band-pass filter parameters were selected with the help of fast kurtogram,and the kurtosis time series were calculated by using sliding kurtosis algorithm for spectrum analysis.In this way,the weak impacts of rolling element bearing with incipient fault can be extracted,which solves the problem of parameter selection of band-pass filter,and extracts the weak impact components in the incipient fault of bearing.Test results show that,compared with the traditional method,the new method has good performance in extracting the weak signal of incipient fault features with strong noise.
In order to solving the problem that the early fault features of rolling bearing are very weak and easy to be destroyed by strong background noise,the zero delay forth-order cumulant(i.e.kurtosis factor) is introduced for describing the degree of weak impacts deviating from Gauss distribution.A new method for incipient fault diagnosis of rolling element bearings is proposed based on EEMD Denoising and Sliding Kurtosis Demodulation(SKD).Firstly,fault signals were decomposed into a group of IMFs with EEMD;Secondly,the IMF best representing the fault information was selected to reconstruct signals using an adaptive optimization algorithm which constructed by information entropy and measure coefficient.By virtue of the spectral kurtosis analysis of the new composite signal.the optimal band-pass filter parameters were selected with the help of fast kurtogram,and the kurtosis time series were calculated by using sliding kurtosis algorithm for spectrum analysis.In this way,the weak impacts of rolling element bearing with incipient fault can be extracted,which solves the problem of parameter selection of band-pass filter,and extracts the weak impact components in the incipient fault of bearing.Test results show that,compared with the traditional method,the new method has good performance in extracting the weak signal of incipient fault features with strong noise.
引文
[1]刘永强,李翠省,廖英英.基于EEMD和自相关函数峰态系数的轴承故障诊断方法[J].振动与冲击,2017,36(2):111-116.
[2]ZHAOHUA WU,NORDEN E.HUANG.ENSEMBLE EMPIRICAL MODE DECOMPOSITION:A NOISE-ASSISTED DATA ANALYSIS METHOD[J].Advances in Adaptive Data Analysis,2011,1(01):1-41.
[3]邱天爽,张旭秀,李小兵,等.统计信号处理:非高斯信号处理及应用[M].北京:电子工业出版社,2004.
[4]杨富春,周晓军,张志刚.基于滑动峰态算法的信号弱冲击特征提取及应用[J].振动与冲击,2009,28(4):103-105+206.
[5]张亿雄,顾海明.Hilbert变换在滚动轴承故障诊断中的应用[J].煤矿机械,2008,29(12):212-214.
[6]潘阳,陈安华,蒋玲莉,等.基于自适应共振解调技术的滚动轴承故障诊断[J].机械科学与技术,2015,34(2):238-241.
[7]彭畅,柏林,谢小亮.基于EEMD、度量因子和快速峭度图的滚动轴承故障诊断方法[J].振动与冲击,2012,31(20):143-146.
[8]张志刚,石晓辉,陈哲明,等.基于改进EMD与滑动峰态算法的滚动轴承故障特征提取[J].振动与冲击,2012,31(22):80-83.
[9]杨富春,周晓军,杨先勇.滑动峰态算法解调性能及应用研究[J].中国机械工程,2009,20(7):803-807.
[2]ZHAOHUA WU,NORDEN E.HUANG.ENSEMBLE EMPIRICAL MODE DECOMPOSITION:A NOISE-ASSISTED DATA ANALYSIS METHOD[J].Advances in Adaptive Data Analysis,2011,1(01):1-41.
[3]邱天爽,张旭秀,李小兵,等.统计信号处理:非高斯信号处理及应用[M].北京:电子工业出版社,2004.
[4]杨富春,周晓军,张志刚.基于滑动峰态算法的信号弱冲击特征提取及应用[J].振动与冲击,2009,28(4):103-105+206.
[5]张亿雄,顾海明.Hilbert变换在滚动轴承故障诊断中的应用[J].煤矿机械,2008,29(12):212-214.
[6]潘阳,陈安华,蒋玲莉,等.基于自适应共振解调技术的滚动轴承故障诊断[J].机械科学与技术,2015,34(2):238-241.
[7]彭畅,柏林,谢小亮.基于EEMD、度量因子和快速峭度图的滚动轴承故障诊断方法[J].振动与冲击,2012,31(20):143-146.
[8]张志刚,石晓辉,陈哲明,等.基于改进EMD与滑动峰态算法的滚动轴承故障特征提取[J].振动与冲击,2012,31(22):80-83.
[9]杨富春,周晓军,杨先勇.滑动峰态算法解调性能及应用研究[J].中国机械工程,2009,20(7):803-807.