摘要
经验小波变换是一种基于Fourier频谱特性,通过构建自适应小波滤波器组来分析复杂多分量信号的方法。该方法能够有效识别信号中的不同模态分量,但由于其Fourier频谱分割问题,在处理噪声及不稳定信号方面有所欠缺。针对这一问题,采用改进的经验小波变换方法,将信号分解为具有物理意义的经验模态。改进的经验小波变换主要考虑被处理信号的频谱形状,通过采用基于顺序统计滤波器(OSF)的包络方法以及遵循三个准则来获取有效峰值的方法,改进Fourier频谱分割过程。将改进的方法应用于滚动轴承故障诊断中,由于改进的经验小波变换能够将振动信号分解为一系列单分量成分,因此在轴承振动信号包络谱中能够清晰的发现故障特征。通过对滚动轴承振动模拟信号和实验信号的分析验证了该方法的有效性。
The empirical wavelet transform(EWT) is a novel method for analyzing the multi-component signals by constructing an adaptive filter bank.Although it is an effective tool to identify the signal components,it has drawback in dealing with some noisy and non-stationary signals due to its coarse spectrum segmentation.Targeting this problem,an enhanced EWT(EEWT) is proposed.In this method,the signal is decomposed into several empirical modes with physical meanings.This method ameliorates the drawback of EWT by taking the spectrum shape of the processed signal into account.It improves the segmentation process by adopting the envelop approach based on the order statistics filter(OSF) and applying three criteria to pick out useful peaks.The envelope spectrums of the extracted empirical modes are applied to rolling bearing fault diagnosis.Because the EEWT can decompose vibration signal into a set of mono-components,fault features can be found clearly in the envelop spectrum.The effectiveness of the proposed method is verified by a simulation signal and a real signal captured from the test rig.
出处
《噪声与振动控制》
CSCD
2018年第1期199-203,共5页
Noise and Vibration Control
基金
上海市科学技术委员会基础研究资助项目(15JC1402600)
