基于EEMD分解和奇异值差分谱理论的轴承故障诊断研究

提出一种基于总体平均经验模态分解(ensemble empirical mode decomposition,EEMD)和奇异值差分谱的轴承故障诊断方法。首先将非平稳的原始轴承振动信号通过EEMD方法分解成若干个平稳的本征模函数(intrinsic modefunction,IMF);由于背景噪声的影响,从各个IMF的频谱中难以准确地得到故障频率。对IMF分量构建Hankel矩阵,并进行奇异值分解,进一步找到奇异值差分谱,根据奇异值差分谱理论对某IMF分量进行消噪和重构,然后再求其频谱,便能准确地得到故障频率。实验结果表明,所提出的方法能有效地应用于轴承的故障诊断。

BEARING FAULT DIAGNOSIS METHOD BASED ON EEMD AND DIFFERENCE SPECTRUM THEORY OF SINGULAR VALUE

A fault diagnosis scheme,which is based on ensemble empirical mode decomposition(EEMD) and difference spectrum theory of singular value,is put forward.Firstly,original acceleration vibration signals are decomposed into a finite number of stationary intrinsic mode functions(IMFs);it is difficult to obtain fault frequencies because of strong background noise.Therefore,to identify the fault pattern,singular value features that extracted from a number of IMFs contain the most dominant fault information.To construct a Hankel matrix of an IMF and do a singular value decomposition(SVD).To move forward a single step,difference spectrum of singular values is obtained.On the basis of difference spectrum theory,de-noising and reconstruction can be done to some IMFs component in order to get its frequency spectrum.Finally,fault frequency can be identified accurately.Practical examples show that the diagnosis approach put forward can identify bearing fault patterns effectively.