基于LCD-MCKD的滚动轴承故障特征提取方法

Feature extraction of fault rolling bearings based on LCD-MCKD

鉴于在复杂工况和强背景噪声环境下,滚动轴承的非线性非平稳信号的特征提取非常困难,导致早期故障难以诊断,提出了一种基于局部特征尺度分解(LCD)和最大相关峭度解卷积(MCKD)的故障特征提取方法.首先,利用LCD对信号进行分解,获得一系列瞬时频率具有物理意义的内禀尺度分量(ISC),选取相关系数较大的ISC分量进行重构;然后,利用MCKD方法对重构信号进行处理,增强冲击信号频率,实现降噪;最后,对经LCD-MCKD处理过的信号进行希尔伯特包络谱分析,验证所提方法的有效性.仿真和实验表明该方法能够有效提取故障特征频率,实现故障诊断.

Feature extraction of nonlinear and non-stationary signals of rolling bearings is very difficult in complex conditions and strong background noise,resulting in early failure difficult to diagnose.An intelligent diagnosis method based on local character-scale decomposition and maximum correlated kurtosis deconvolution was proposed.Firstly,the measured vibration signals were processed with local characteristic-scale decomposition(LCD)and decomposed into a series of intrinsic scale component(ISC).Then,The ISC components with large correlation coefficient were selected for reconstruction.The maximum correlated Kurtosis deconvolution(MCKD)method was utilized to process the reconstructed signals,for highlighting the impact signal frequency to achieve noise reduction.Finally,the Hilbert envelope spectrum analysis was performed on the signal obtained by LCD-MCKD,and verified the validity of the proposed method.Simulation and experiments show that this method can effectively extract the fault characteristic frequency and be applied in fault diagnosis.