IEWT和FSK在齿轮与滚动轴承故障诊断中的应用

Gear and Rolling Bearing Fault Diagnosis Based on Improved EWT and Fast Spectral Kurtosis Filtering

改进的经验小波变换方法(improved empirical wavelet transform,简称IEWT)是一种新的自适应性信号处理方法,将这种方法和快速谱峭度(fast spectral kurtosis,简称FSK)相结合,进行齿轮与滚动轴承的故障诊断。首先,采用IEWT对信号进行分解,筛选出故障特征最为明显的2个分量并重构信号;其次,对重构信号进行快速谱峭度滤波;最后,对滤波后的信号进行包络谱分析,提取出信号的故障特征。分析齿轮断齿及滚动轴承故障信号,与直接包络谱和基于EMD经验模态分解(empirical mode decomposition,简称EMD)方法的FSK滤波包络谱分析方法相比可知,采用IEWT处理后再进行FSK滤波的信号进行包络谱分析更具有区分性,可有效识别齿轮和滚动轴承的故障特征。

Improved empirical wavelet transform(IEWT)is a new kind of self-adaptation method of signal analysis.Combine this method with fast spectral kurtosis(FSK)filtering,and the gear and rolling bearing fault diagnosis can be achieved.Firstly,the signal is decomposed using the IEWT method,and the two components which have the most obvious fault characteristics are extracted and reconstructed.Then,the reconstructed signal is filtered using the FSK filtering method.Lastly,the filtered signal is analyzed using spectral envelope method and the fault characteristics of signal are extracted.By analyzing the broken gear teeth and rolling bearing fault signals,it is indicated that the method based on IEWT and FSK of envelope spectrum analysis is more distinctive than spectral envelope method and envelope spectrum analysis based on EMD and FSK.It can effectively identify the fault characteristics of gear and rolling bearings.