摘要
为更好的表征电机轴承的退化状态,对电机轴承退化特征提取方法进行了研究。结合变分模态分解(Variational modal decomposition, VMD)和信息熵理论,提出了基于VMD分解谱熵的退化状态识别方法。对不同损伤程度的轴承振动信号进行VMD分解,分别计算其在不同尺度下的复杂度度量能谱熵、奇异谱熵和边际谱熵,以其作为退化特征向量。通过建立相关向量机退化状态识别模型实现轴承的退化状态识别。仿真信号和轴承实测信号均验证了VMD分解谱熵对轴承退化状态的表征能力。
In order to characterize the degradation state of motor bearing,the degradation feature extraction method are studied. Combining variational modal decomposition (VMD) and information entropy,a degradation state identification method based on VMD decomposition spectral entropy was proposed. The VMD decomposition was performed for bearing vibration signals with different damage levels,and its complexity indicators of power spectral entropy,singular spectral entropy and marginal spectral entropy in different scale were computed. The three indicators were taken as degradation feature vector. Then,the degradation state identification model is established based on relevance vector machine to allowing the degradation state of bearing to be identified. The analysis results of simulation signal and practical bearing vibration signal demonstrate the ability of the proposed method.
作者
段永彬
张玉芝
安建良
张前图
DUAN Yongbin;ZHANG Yuzhi;AN Jianliang;ZHANG Qiantu(Department of Automobile EngineeringHebei College of Industry and Technology,Shijiazhuang 050091,China;Military Represent Office of Jiangjin District,Chongqing 401120,China)
出处
《机械设计与研究》
CSCD
北大核心
2019年第4期101-104,共4页
Machine Design And Research
关键词
变分模态分解
谱熵
退化状态
特征提取
轴承
variational modal decomposition
spectral entropy
degradation state
feature extraction
bearing