小波变换及经验模式分解方法在电机轴承早期故障诊断中的应用  

STUDY ON THE METHOD OF INCIPIENT MOTOR BEARING FAULT DIAGNOSIS BASED ON WAVELET TRANSFORM AND EMD

作　　者：罗忠辉[1] 薛晓宁[1] 王筱珍[1] 吴百海[2] 何真[1] 

机构地区：[1]湛江海洋大学工程学院,广东省湛江市524025 [2]广东工业大学机电学院,广东省广州市510090

出　　处：《中国电机工程学报》2005年第14期125-129,共5页Proceedings of the CSEE

摘　　要：电机轴承早期故障的有效诊断是实现安全生产、避免大事故的技术前提。文中用高精度加速度传感器采集电机轴承振动信号,采用小波变换实现信噪分离,提取淹没在噪声背景中的早期故障特征信息,然后对提纯的信号进行经验模式分解(EMD)而得到若干个基本模态分量(IMF),再计算各基本模态分量的频谱。理论及试验研究结果表明:按此方法得到的各基本模态分量的频谱突显了轴承的故障特征信息,能有效诊断出轴承的早期故障。Incipient fault diagnosis in bearings is the technical prerequisite for safe production and to avoiding accidents. A highly precise acceleration transducer is used to sample vibration signals in bearings. Incipient bearing fault characteristic signals obscured by noise background are extracted by using wavelet decomposition method. The extracted signals are decomposed by means of Empirical Mode Decomposition（EMD） to obtain several intrinsic mode functions（IMFs）. And the frequency spectra of IMFs are calculated finally. The results of theoretical and experiments research show that the spectra of IMFs obtained by the above method reveal the characteristic information in bearings dearly,which can be used to detect incipient faults in bearings.

关 键 词：电机轴承 早期故障诊断 小波变换 经验模式分解 基本模态分量 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置] TH165[自动化与计算机技术—控制科学与工程]