基于ELMD和1.5维谱的滚动轴承早期故障诊断方法

Early Fault Diagnosis of Rolling Bearing Based on ELMD and 1.5 Dimension Spectrum

滚动轴承出现早期故障时,故障特征十分微弱,伴随严重的噪声干扰导致其故障特征难以识别,针对这一问题,提出了一种总体局部均值分解(Ensemble Local Mean Decomposition,ELMD)和1.5维谱相结合的滚动轴承故障诊断新方法。该方法首先运用ELMD对振动信号进行分解,得到一系列乘积函数(product function,PF)分量,然后根据峭度准则以及相关系数准则提取一个包含主要故障信息的PF分量,最后对提取的PF分量进行1.5维谱分析,通过分析谱图中突出成分以确定轴承故障类型。通过仿真信号和工程实验数据分析验证了该方法的有效性。

When the incipient fault of rolling bearing occurs,the fault characteristics are very weak.Accompanied by severe noise interference,the fault characteristics are difficult to be identified.In order to solve this problem,a new method of rolling bearing fault diagnosis based on Ensemble Local Mean Decomposition(ELMD)and 1.5 dimension spectrum is proposed.Firstly,the ELMD method is used to decompose the vibration signal,a signal of a finite number of product function(PF)is obtained.Then,according to the kurtosis criterion and correlation criterion of each component,a PF component containing important fault information is extracted.Finally,the extracted PF component is analyzed by 1.5 dimension spectrum,fault type of bearing can be determined by analyzing the prominent components in 1.5 dimension spectrum.The effectiveness of the proposed method is verified by signal simulation and engineering experiments.