China Mechanical Engineering

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GVMD and Its Applications in Composite Fault Diagnosis for Gearboxes

YANG Yu;LUO Peng;CHENG Junsheng   

  1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,Hunan University,Changsha,410082
  • Online:2017-05-10 Published:2017-05-04

广义变分模态分解及其在齿轮箱复合故障诊断中的应用

杨宇;罗鹏;程军圣   

  1. 湖南大学汽车车身先进设计制造国家重点实验室,长沙,410082
  • 基金资助:
    国家重点研发计划资助项目(2016YFF0203400);
    国家自然科学基金资助项目(51575168,51375152);
    智能型新能源汽车国家2011协同创新中心、湖南省绿色汽车2011协同创新中心资助项目

Abstract: According to the defects of the VMD that its penalty parameters and number of components were based on the prior knowledges in the processes of the actual applications, based on the VMD an improved method, namely, the GVMD was proposed herein. This method held the potentials to overcome the deciencies of VMD, and it might reduce the subjective influences on the decomposition results. By decomposing the signal into non recursive and variational modal, the method might effectively separate the harmonic frequency components that were similar to each other and had good robustness. It was applied in the composite fault diagnosis of the gearboxex, and the simulation results and test verify the validity of this method.

Key words: generalized variational mode decomposition(GVMD), gearbox, composite fault diagnosis, feature extraction

摘要: 针对变分模态分解在实际应用过程中需要根据先验知识确定惩罚函数和分量分解个数这一缺陷,提出了一种改进方法,即广义变分模态分解方法。该方法减少了人为因素对分解结果造成的主观影响,将信号分解转化为非递归、变分模态分解方式,能够有效分离频率成分相近的谐波分量,且对信噪比较小的信号有着良好的鲁棒性。将该方法应用于齿轮箱复合故障诊断中,仿真和实验的结果验证了该方法的有效性。

关键词: 广义变分模态分解, 齿轮箱, 复合故障诊断, 特征提取

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