强噪声背景下频率加权能量算子和变分模态分解在轴承故障提取中的应用  

A frequency-weighted energy operator and variational mode decomposition for bearing fault detection

作　　者：徐元博[1] 蔡宗琰[1] 胡永彪[1] 丁凯 XU Yuan-bo;CAI Zong-yan;HU Yong-biao;DING Kai(Key Laboratory of Road Construction Technology and Equipment of MOE,Chang′an University,Xi’an 710064,China)

机构地区：[1]长安大学道路施工技术与装备教育部重点实验室,陕西西安710064

出　　处：《振动工程学报》2018年第3期513-522,共10页Journal of Vibration Engineering

摘　　要：从机械系统中传出的信号通常包含着不同的叠加振动成分,包括有用信息以及不可避免的背景噪声和其他频率干扰,因此波形较为复杂,并且其幅值和频率会随着时间发生变化。当背景环境较为复杂或噪声较大时,从混合信号中提取出的轴承故障特征信号更是如此。对于此类信号,模态分解算法不仅可以去除大量的高频噪声,而且还能将振动信号分解成一系列具有单一成分的模态分量,从而更好地发现振动信号的物理意义。引入一种新的轴承故障特征提取方法,首先利用变分模态分解算法先将故障信号分解为若干个成分单一的模态分量;然后利用一种新的能量算子——频率加权能量算子对含有故障频率的模态分量进行处理,得到其能量谱从而提取出轴承故障特征频率;最后以一种常见的振动筛分设备振动筛为实际案例,对其轴承故障特征进行提取,并通过对比,说明了该算法的优越性和实用性。The signals measured from mechanical systems are usually composed of many different oscillations,including the useful information,the inevitable ambient noise and other vibration interferences,so the waveform is usually very complicated.Additionally,these oscillations are characterized by time-varying amplitudes and frequencies.It is a challenge,especially in the presence of strong ambient noises or vibration interferences,to extract the characteristic frequencies of the bearing fault.The mode decomposition algorithm is an effective method for solving this problemas it can remove the ambient noises at the greatest extent and simultaneously decompose a given signal into a set of single-mode components,thus extracting useful information from the vibration signals.A new method for bearing fault detection is presented.At first,the variational mode decomposition,which is a new mode decomposition method,is used to decompose a fault signal into a set of single-mode components.Then,a modified energy operator method named frequency-weighted operator is employed to process the fault component.Finally,the energy spectrum and instantaneous frequency of the fault frequency are obtained.The results of bearing fault experiments for an oscillating screen indicate that the proposed method can effectively extract fault features,demonstrating its feasibility and superiority over other existing methods.

关 键 词：故障诊断 滚动轴承 振动筛 变分模态分解 频率加权能量算子 

分 类 号：TH165.3[机械工程—机械制造及自动化] TH133.33