基于LMS和Fast-Kurtogram的滚动轴承早期故障诊断
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摘要
针对滚动轴承早期故障特征提取困难的问题,提出一种LMS(Least Mean Square,LMS)算法降噪、FastKurtogram选频和共振解调技术相结合的滚动轴承故障诊断方法。首先对采集到的信号进行自适应降噪,减弱背景噪声的影响;然后利用谱峭度值对故障信号中瞬态成分敏感的特性,通过计算降噪后信号的快速峭度图,确定滤波器最优频带中心和带宽;最后进行共振包络解调提取出滚动轴承早期故障特征。通过仿真和实验验证分析,验证了该方法在滚动轴承早期故障诊断中的适用性和有效性。
Due to the difficulty of early fault features extraction of rolling bearings, a new fault diagnosis method for rolling bearings based on LMS algorithm noise reduction, Fast-Kurtogram frequency selection and resonance demodulation technology is proposed. First of all, the adaptive noise reduction is used to reduce the effect of the background noise. Then,based on the characteristics of spectral kurtosis, which is sensitive to the transient components of the faulty signal, the optimal band center and bandwidth of filter can be determined by plotting the Fast-Kurtogram of the denoised signal.Finally, the resonance envelope demodulation is used to extract the early fault characteristics of the rolling bearing. The feasibility and efficiency of this proposed method for the early fault diagnosis of rolling bearing have been verified by simulation and experiments.
Due to the difficulty of early fault features extraction of rolling bearings, a new fault diagnosis method for rolling bearings based on LMS algorithm noise reduction, Fast-Kurtogram frequency selection and resonance demodulation technology is proposed. First of all, the adaptive noise reduction is used to reduce the effect of the background noise. Then,based on the characteristics of spectral kurtosis, which is sensitive to the transient components of the faulty signal, the optimal band center and bandwidth of filter can be determined by plotting the Fast-Kurtogram of the denoised signal.Finally, the resonance envelope demodulation is used to extract the early fault characteristics of the rolling bearing. The feasibility and efficiency of this proposed method for the early fault diagnosis of rolling bearing have been verified by simulation and experiments.
引文
[1]荆双喜,李新华,朱昆鸣,等.基于最大相关峭度解卷积的滚动轴承早期故障诊断[J].河南理工大学学报,2018,36(1):81-85.
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[11]马新娜,杨绍普.典型谱峭图在共振解调方法中的应用[J].振动、测试与诊断,2015,35(6):1140-1144.
[12]唐贵基,刘尚坤.基于VMD和谱峭度的滚动轴承早期故障诊断方法[J].中国测试,2017,43(9):112-117.
[13]苏文胜,王奉涛,张志新,等. EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J].振动与冲击,2010,29(3):18-21.
[14]西蒙·赫金.自适应滤波器原理[M].北京:电子工业出版社,2016.
[15] ANTONI J. The spectral kurtosis:a useful tool for characterizing non-stationary signals[J]. Mechanical Systems and Signal Processing, 2006, 20(1):282-307.
[16]毕果.基于循环平稳的滚动轴承及齿轮微弱故障特征提取应用研究[D].上海:上海交通大学,2007.
[2]姜战伟,郑近德,潘海洋,等.基于改进多尺度熵与VPMCD的滚动轴承故障诊断[J].噪声与振动控制,2017,37(3):156-161.
[3]刘建春,胡俊锋,熊国良,等.基于谱峭度和Teager能量算子的轴承故障特征增强[J].噪声与振动控制,2018,38(1):182-187.
[4]韩清凯,于晓光.基于振动分析的现代机械故障诊断原理及应用[M].北京:科学出版社,2010.
[5]王梦奇,马增强,王建东.基于局部均值分解和共振解调的滚动轴承故障诊断[J].济南大学学报,2018,32(1):13-19.
[6]朱汉明.基于EMD和共振解调的滚动轴承故障诊断方法研究[D].上海:上海师范大学,2011.
[7]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.
[8]李宏坤,杨蕊,任远杰,等.利用粒子滤波与谱峭度的滚动轴承故障诊断[J].机械工程学报,2017,56(3):63-72.
[9] DWYER R F. Detection of non-gaussian signals by frequency domain kurtosis estimation[C]. International Conference On Acoustics, Speech, and ignal Processing,Boston,1983:607-610.
[10]王宏超,陈进,董广明,等.基于快速kurtogram算法的共振解调方法在滚动轴承故障特征提取中的应用[J].振动与冲击,2013,32(1):35-37.
[11]马新娜,杨绍普.典型谱峭图在共振解调方法中的应用[J].振动、测试与诊断,2015,35(6):1140-1144.
[12]唐贵基,刘尚坤.基于VMD和谱峭度的滚动轴承早期故障诊断方法[J].中国测试,2017,43(9):112-117.
[13]苏文胜,王奉涛,张志新,等. EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J].振动与冲击,2010,29(3):18-21.
[14]西蒙·赫金.自适应滤波器原理[M].北京:电子工业出版社,2016.
[15] ANTONI J. The spectral kurtosis:a useful tool for characterizing non-stationary signals[J]. Mechanical Systems and Signal Processing, 2006, 20(1):282-307.
[16]毕果.基于循环平稳的滚动轴承及齿轮微弱故障特征提取应用研究[D].上海:上海交通大学,2007.