基于VMD和变尺度多稳随机共振的微弱故障信号特征提取方法

针对强噪声背景下旋转机械早期故障诊断的难题,提出一种基于变分模态分解与变尺度多稳随机共振的微弱故障信号特征提取方法。首先应用参数优化的变分模态分解(variational mode decomposition, VMD)算法对微弱故障信号进行分解, 得到若干本征模态分量(intrinsic mode function, IMF);然后通过峭度准则筛选出其中峭度最大的IMF分量;最后对该IMF分量进行变尺度多稳随机共振, 实现微弱故障信号的增强。实例表明:在强噪声背景下,利用参数优化VMD分解与变尺度多稳随机共振相结合的方法,可以有效提取出微弱信号特征频率,实现旋转机械故障状态的准确判断。

A New Feature Extraction Method of Weak Fault Signal Based on VMD and Re-scaling Multi-stable Stochastic Resonance

To realize the feature extraction of rotating machinery in the strong noise environment, a feature extraction method of weak fault signal based on variational mode decomposition and re-scaling multi-stable stochastic resonance is proposed. The first application of parameter optimization of variational mode decomposition (VMD) algorithm for fault signal is decomposed into several intrinsic mode functions (IMFs), and then through the kurtosis criterion and find the maximum kurtosis of IMF component, finally the characteristic frequency of the IMF component through the re-scaling multi-stable stochastic resonance system will be enhanced, which is easily and clearly detected. The simulation analysis and experiments reveal that, in the strong background noise, the combination of optimized VMD algorithm and the method of re-scaling multi-stable stochastic resonance system, can effectively extract weak feature frequency information and realize the accurate judgment of the rotating machinery fault state.