基于改进变分模态分解及多重分形的轴承信号非线性分析

NONLINEAR ANALYSIS OF BEARING SIGNAL BASED ON IMPROVED VARIATIONAL MODAL DECOMPOSITION AND MUTI FRACTAL

考虑轴承故障初期具有特征信号微弱、易受噪声干扰以及非线性强等特点。基于分形盒维数提出改进变分模态分解提取轴承故障信号非线性信息方法(Improved Variational Mode Decomposition for Nonlinear Features Extraction,IVMD-NFE)。又因非线性信号的多测度性,采用多重分形去趋势波动分析(Multifractal Detrended Fluctuation Analysis,MF-DFA)法,研究各故障信号的多重分形特征。以滚动轴承实验数据为研究对象,采用IVMD-NFE与MF-DFA方法对轴承初期信号进行故障分析与诊断。结果表明:采用IVMD-NFE方法提取的信号可较大程度滤除噪声且具有更低的分形盒维数,提取的非线性特征更具代表性;轴承故障信号呈现多重分形特征,外圈故障的奇异指数α;最大,非线性最强,保持架故障时α;最小,非线性最弱,说明通过数据复杂度可较好反应轴承运行状态,而采用VMD或直接对原始信号进行处理的方法,未能提取有效非线性特征,导致故障区分失败。

Some characteristics of bearing initial signals are considered including unobvious features,susceptibility to noise interference and strong nonlinearity when a bearing was damaged.Based on the fractal box dimension,an improved variational mode decomposition for nonlinear features extraction of bearing fault signals(IVMD-NFE)is proposed.Because of the multi-measurement of nonlinear signals,the multifractal detrended fluctuation analysis(MF-DFA)method is used to study the multifractal characteristics of each fault signal.Taking the experimental data of rolling bearing as the research object,IVMD-NFE and MF-DFA method were used to analyze and diagnose the initial bearing signal.The results show that the signal extracted by the IVMD-NFE method can filter out noise to a greater extent and has lower fractal box dimension,and the extracted nonlinear characteristics are more representative;The fault signals of bearing exhibit multi-fractal features,the outer ring fault has the largest singularity index and strongest nonlinearity,and the cage fault is the smallest and weakest nonlinearity,indicating that the complexity of the data can better reflect the running state of the bearing.However,the method of using VMD or directly processing the original signal fails to extract effective nonlinear features,resulting in failure to distinguish faults.