噪声与振动控制2018，Vol.38Issue(1)：199-203,5.DOI:10.3969/j.issn.1006-1355.2018.01.039
改进的经验小波变换在滚动轴承故障诊断中的应用
Application of Enhanced Empirical Wavelet Transform to Rolling Bearings Fault Diagnosis
摘要
Abstract
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.关键词
振动与波/改进经验小波变换/顺序统计滤波器/三种筛选准则/轴承故障诊断Key words
vibration and wave/EEWT/OSF/three filtering criteria/rolling bearings fault diagnosis分类
机械制造引用本文复制引用
朱艳萍,包文杰,涂晓彤,胡越,李富才..改进的经验小波变换在滚动轴承故障诊断中的应用[J].噪声与振动控制,2018,38(1):199-203,5.基金项目
上海市科学技术委员会基础研究资助项目(15JC1402600) （15JC1402600）
