敏感SVD和EEMD的故障诊断方法研究

在噪声干扰下有效提取振动信号所包含的微弱故障特征,是轴承故障诊断的关键问题,提出了一种基于敏感奇异值分解(SSVD)和总体平均经验模态分解(EEMD)的故障诊断方法.对时域振动信号进行敏感SVD分析,通过敏感因子选择反映故障冲击特征的敏感SVD分量,并利用定位因子定位分量信号所对应奇异值进行振动信号重构,以滤除噪声干扰;对降噪信号进行EEMD,根据峭度准则选取故障信息丰富的敏感固有模态分量(IMF),有效提取局部微弱故障信息;利用Teager-Kaiser能量算子(TKEO)计算故障信息的瞬时能量,并对其进行频谱分析,获取故障特征频率,以识别故障类型.方法应用于轴承故障诊断,实验证明了所提方法的有效性.

Study on fault diagnosis method based on SSVD and EEMD

How to extract weak fault characteristics from vibration signals with noise is a key problem of bearing fault diagnosis. A novel method based on sensitive singular value decomposition(SSVD)and ensemble empirical mode decomposition(EEMD)is proposed,aiming at the above problem. SSVD is applied to vibration signals which are analyzed in time domain,SSVD component signals which reflect fault impact characteristics are selected via the sensitive factor,and the corresponding singular values for vibration signal reconstruction are determined by using positioning factor,noise will be removed effectively through above steps. The de-noised signals are decomposed by EEMD,so that intrinsic mode function(IMF)components which includes sensitive fault information can be selected according to the kurtosis criterion,through which the local weak fault information of vibration signals can be effectively extracted. Instantaneous energy of fault information is calculated by using Teager-Kaiser energy operator (TKEO),and the spectrum analysis is performed to obtain fault characteristic frequency,will be used to identify type of fault. The experimental results prove the effectiveness of this method of rolling bearing fault diagnosis.