摘要
针对滚动轴承早期故障振动信号受噪声影响、总体经验模态分解(EEMD)参数不易获取的问题,提出了基于改进EEMD和谱峭度的滚动轴承故障诊断方法。首先提取信号高频成分及设置期望分解误差确定EEMD参数,利用EEMD将信号分解为若干个本征模态分量(IMF),依据峭度准则选取相应分量进行重构以突出故障信息、提高信噪比;然后利用快速谱峭度图来选取带通滤波器的参数;最后对滤波信号进行能量算子解调分析。该方法应用到实测数据中的结果表明,其不仅能够自适应确定EEMD参数,降低了噪声的影响,还能清晰、准确地提取出故障特征频率,实现了滚动轴承故障的精确诊断。
In order to solve the problems that the early fault vibration signals of rolling bearing affected by the noise,the parameters of ensemble empirical mode decomposition(EEMD)are not easy to be obtained.A method for fault bearings of rolling bearings based on improved EEMD and spectrum kurtosis(SK)was presented.Firstly,obtaining the amplitude coefficient of added noise by extracting the high frequency information,the number of ensemble members is obtained by calculating the expectation error,the fault signal was decomposed into several intrinsic mode function(IMF)by improved EEMD,the IMFs were reconstructed based on kurtosis criterion.Then,the central frequency and bandwidth of a band-pass filter were determined with spectral kurtosis.Last,the filtered signal was analyzed by using energy operator demodulation spectrum.The results demonstrate that the proposed method can not only solve the problems such as losing fault information and leaving noise due to the mode mixing in the process of denoising,but also extract the fault frequency accurately.
作者
马增强
张俊甲
王梦奇
阮婉莹
MA Zengqiang;ZHANG Junjia;WANG mengqi;RUAN Wanying(Electrical and Electronics Engineering, Shijiazhuang Railway University, Shijiazhuang Hebei 050043, China)
出处
《图学学报》
CSCD
北大核心
2017年第5期663-669,共7页
Journal of Graphics
基金
国家自然科学基金项目(11372199
51405313
11572206)
河北省自然科学基金项目(A2014210142)
关键词
滚动轴承
经验模态分解
谱峭度
故障诊断
rolling bearing
ensemble empirical mode decomposition
spectrum kurtosis
fault diagnosis
