结合局域均值分解(Local mean decomposition, LMD)方法和Wigner高阶矩谱,提出了一种基于局域均值分解的Wigner高阶矩谱的机械故障诊断方法,该方法保留了LMD和Wigner高阶矩谱的所有优良性能,有效地抑制了Wigner高阶矩谱的交叉项的干扰。仿真结果表明,提出的方法优于直接Wigner高阶矩谱和Choi-Williams核滤波后的Wigner高阶矩谱。最后,将本文方法应用到轴承故障诊断中,实验结果进一步验证了该方法的的有效性。
Abstract:Combining local mean decomposition (LMD) and Wigner higher moment spectrum (WHOS), a machine fault diagnosis method based on LMD and WHOS is proposed. The proposed method reserves the advantages of LMD and WHOS, and the cross-term in the WHOS can be effectively suppressed. The simulation result shows that the proposed method is superior to direct WHOS and Choi-Williams kernel filter WHOS. At last, the proposed method is succesfully applied to the rolling bearing fault diagnosis, the experiment result further verifies the effectivity of the proposed method.
刘卫兵;李志农;蒋静. 基于局域均值分解和Wigner高阶矩谱的机械故障诊断方法的研究[J]. , 2010, 29(6): 170-173.
Liu Weibing;Li Zhinong;Jiang Jing. Machine fault diagnosis method based on local mean decomposition and Wigner higher moment spectrum. , 2010, 29(6): 170-173.