摘要
针对共振解调中带通滤波器参数的选取通常比较困难,以及滚动轴承早期微弱故障信号通常被强烈的背景噪声淹没,为此,提出了使变分模态分解(variational mode decomposition,VMD)和谱峭度法共同作用来处理故障信号的方法。首先要重构故障信号,利用VMD分解得到故障信号的本征模态分量(intrinsic mode function,IMF),再计算各分量对应的峭度值对其自适应重构。然后,对重构信号进行快速谱峭度分析,并据此设计带通滤波器。最后,根据重构信号共振解调后的谱线即可准确判断轴承故障。通过处理实测数据进行诊断,结果表明了该方法较传统共振解调法诊断结果更精确。由此可见,谱峭度法在滤波器参数选择上具有可靠性,以及VMD与谱峭度结合能够降低噪声干扰提取微弱故障信号。
In order to solve the problem that the band-pass filter parameters in resonance demodulation are difficult to select and the fault signals of rolling bearing in early failure period aredrowned in strong background noise, thefauh diagnosis methodcombining variational mode decomposition （VMD）withspectral kurtosis is proposed. Firstly, the fault signals need to bereeonstructed self-adaptively, so several intrinsic mode function （IMF） are obtained by VMD, and adaptive reconstruction is performed by computing the kurtosis of IMFs. Next,we can analyze the reconstructed signals by spectral kurtosis and design the band-pass filter. Finally, the working status of rolling bearingis identified through the resonance demodulation spectrum of reconstructedsignal. By processing real data, the results show that the method is more accurate than traditional resonance demodulation in diagnosing the fault of rolling bearing. Thus, it can be seen, the spectral kurtosis is reliable in selection of the filter parameters, and combining VMD and spectral kurtosis can reduce the noise and extract weak fault signal.
出处
《电子测量与仪器学报》
CSCD
北大核心
2017年第11期1782-1787,共6页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(11227201
11372199)
河北省自然科学基金(A2014210142)资助项目
作者简介
马增强（通讯作者），分别在1998、2001年于河北工业大学获得学士学位和硕士学位，2011年于北京交通大学获得博士学位，现为石家庄铁道大学教授，主要研究方向为机车车辆状态监测与故障诊断。E-mail：mzqlunwen@126．com
