基于经验小波变换的复杂强噪声背景下弱故障检测方法  

Weak fault detection method in complex strong noise condition based on empirical wavelet transform

作　　者：陈志新[1] 刘鑫[1] 卢成林[1] 马向国[1] 

机构地区：[1]北京物资学院物流学院,北京101149

出　　处：《农业工程学报》2016年第20期202-208,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金青年基金(51205027);2012年度北京市委组织部优秀人才培养资助个人项目D类(2012D005009000002);2014年度北京物资学院高级别科研项目培育基金(0541502707)

摘　　要：针对复杂强噪声背景下的非平稳振动信号的弱故障和复合故障检测的难题,引入经验小波变换(empirical wavelet transform,EWT)以提高故障确诊率,并提出一种基于EWT的复杂强噪声背景下弱故障的检测方法。EWT能够通过完全自适应小波基提取信号的固有模式,与经典小波变换一样具有完备的理论基础。通过对含有复杂强噪声的仿真信号和实际信号进行EWT分析,并对比经验模态分解,验证了基于EWT的复杂强噪声背景下弱故障检测的可行性和有效性。该研究可为复杂工况下机械设备的弱故障和复合故障检测以及故障特征提取提供参考。Empirical wavelet transform（EWT） is able to extract the intrinsic modes of the signal by completely adaptive wavelet basis,which has a complete theoretical base as well as the classical wavelet transform.When the large-scale mechanical equipment in the industrial field is diagnosed and analyzed,and the analyzed vibration signal collected from the equipment often contains complex strong noise,especially a lot of pulse noise.Some recent methods,like the empirical mode decomposition（EMD）,propose to decompose a signal accordingly to its contained information.Even though its adaptability seems to be useful for many applications,the main issue with this approach is its lack of theory.Using the adaptive methods to analyze a signal is of great significance to find sparse representations in the context of fault diagnosis.Aiming at the complicated problem of detecting nonstationary vibration signal of weak fault and compound fault with a large amount of background noise,EWT is introduced to improve the accurate diagnosis rate.A detection method for weak faults in complex strong noise condition based on EWT is proposed.By using the peak characteristic of autocorrelation function to judge the periodicity of the decomposed signals,the most obvious decomposition signal is being as the characteristic signal to be detected.The steps of this method are as follows：1） The original signal is decomposed by EWT;2） The first sub signal is decomposed continuously by EWT,and then the trend signal in the original signal is obtained until the variance change is less than 0.01;3） Using the peak characteristic of autocorrelation function to judge the periodicity of each signal,the most obvious signal is the characteristic signal.The EWT analysis of the simulated signal with complex strong noise and the actual signal is carried out,and by the comparison with the EMD,the feasibility and effectiveness of weak fault detection by EWT are verified.Finally,the conclusions are obtained through this research：1） To the unsteady flu

关 键 词：振动 故障检测 齿轮 噪声 经验小波变换 自适应信号分解 经验模态分解 

分 类 号：TH133.3[机械工程—机械制造及自动化] TH17