基于ITD和敏感SVD的故障诊断方法研究

A study on fault diagnosis method based on ITD and SVD

如何在含有噪声的振动信号中提取故障特征,是轴承故障诊断的关键问题,为此本文提出一种基于本征时间尺度分解(Intrinsic Time-scale Decomposition,ITD)和敏感奇异值分解(Sensitive Singular Value Decomposition,SSVD)的故障诊断方法.首先对时域振动信号进行ITD预处理,并根据峭度准则选取包含故障信息的敏感旋转(Proper Rotation,PR)分量用于振动信号重构,以凸显振动信号局部特征;然后对此时频信号进行敏感SVD分析,通过敏感因子及定位因子选择敏感SVD分量重构信号,以滤除噪声干扰,提取微弱故障信息;最后利用Teager-Kaiser能量算子(Teager-Kaiser Energy Operator,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,用于识别故障类型.将此方法应用于轴承故障诊断,实验证明了所提方法的有效性.

How to extract fault characteristics from the vibration signals with noise is a key problem to the bearing fault diagnosis. A novel method based on Intrinsic Time- scale Decomposition（ITD） and Sensitive Singular Value Decomposition（SSVD） was pro- posed in this paper aiming to solve the above problem. Firstly, ITD was used to pre- process the signals in the time domain, so that the Proper Rotation（PR） components which included sensitive fault information are able to be selected according to the kurto- sis criterion, which were then used for vibration signal reconstruction in order to high- light the local characteristics of vibration signals- secondly, SSVD was applied to the time-frequency signals to remove noise and extract the weak fault information by using the sensitive factor and the positioning factor- finally, the instantaneous energy of the fault information was calculated by using Teager-Kaiser Energy Operator（TKEO）, and the spectrum analysis was performed to obtain the fault characteristic frequency, which would be used to identify the type of fault. The experiment results demonstrated the effectiveness of this method of rolling bearing fault diagnosis.