基于变分态分解与灰狼优化支持向量机的齿轮箱故障诊断

由于行星齿轮齿轮箱的振动信号具有非平稳、非线性特性，在复杂工况下，会对其早期微弱的故障信号造成干扰，不能正确地识别出故障信息。为解决以上问题，采用基于变分模态分解(variational mode decomposition, VMD)与灰狼优化支持向量机的故障诊断方法。利用中心频率近似方法，求解出了变分模态分解的参数K,对分解出的本征模态函数(intrinsic mode function, IMF)分量进行相关性分析，优选出分量进行信号重构。将重构信号进行故障特征提取，利用灰狼优化支持向量机的方法进行故障模式识别。实验结果表明：采用所提方法对行星齿轮箱的故障识别准确率达到99.375%。

Gearbox fault diagnosis based on variational decomposition and Grey Wolf optimized support vector machine

Because the vibration signal of planetary gear box has non-stationary and nonlinear characteristics, it will cause interference to its early weak fault signal under complex working conditions, and the fault information can not be correctly identified. In order to solve the above problems, this paper adopts a fault diagnosis method based on variational mode decomposition and gray Wolf optimized support vector machine. Using the central frequency approximation method, the parameter K of variational mode decomposition is solved, and the correlation analysis of the decomposed intrinsic mode components (IMF) is carried out, and the IMF components are selected for signal reconstruction. The reconstructed signals are extracted for fault features and input into Grey Wolf optimized support vector machine for fault classification. The experimental results show that the fault identification accuracy of the proposed method for planetary gearbox is 99.375%.