基于VMD-Hilbert边际谱能量熵和SVM的高压断路器机械故障诊断

Mechanical fault diagnosis for high voltage circuit breakers based on VMD-Hilbert marginal spectrum energy entropy and SVM

针对高压断路器分、合闸动作过程中产生的振动信号持续时间短暂及强烈的非线性非平稳性,导致的特征提取困难问题,提出一种变分模态分解(VMD)-希尔伯特(Hilbert)边际谱能量熵,及支持向量机(SVM)的高压断路器振动信号组合特征提取和机械故障诊断方法。采用VMD对高压断路器振动信号进行分解,得到一系列反映振动信号局部特性的本征模态函数(IMF);对IMF进行Hilbert变换,并求取对高压断路器机械状态变化敏感的Hilbert边际谱能量熵作为特征向量;将特征向量输入到SVM分类器,实现高压断路器机械故障的智能诊断。试验结果表明:该方法能够准确识别高压断路器的常见机械故障类型,具有一定的工程应用价值。

In this paper,a feature extraction method and fault diagnosis for high voltage circuit breakers(HVCBs) is presented and discussed. The vibration signals are nonlinear and time-varying since the complicated structure and extremely fast operation of HVCBs,which makes the extraction and selection of sensitive features for fault diagnosis difficult. Therefore,it is of vital importance to explore a new vibration feature extraction algorithm to improve the accuracy of fault diagnosis for HVCBs. A combination feature extraction method based on variational mode decomposition(VMD) and Hilbert marginal spectrum energy entropy,and support vector machine(SVM) for the diagnosis of HVCB’s mechanical condition is presented and clearly discussed. Vibration signals were decomposed into several intrinsic mode functions(IMFs) by using VMD. Marginal spectral energy entropies of IMFs(which vary with different fault types of HVCB) were obtained and served as feature vectors for the SVM classifier for the diagnosis of HVCB.Experimental results indicate that the proposed method can accurately identify the common mechanical faults of HVCB and has potential of practical application.