基于包络谱稀疏度和最大相关峭度解卷积的滚动轴承早期故障诊断方法

滚动轴承处于早期故障阶段时,特征信号微弱,并且受环境噪声影响严重,因此故障特征提取困难。针对这一问题,将最大相关峭度解卷积算法应用于轴承故障诊断,并通过包络谱稀疏度来筛选最佳解卷积周期参数,提出了基于包络谱稀疏度和最大相关峭度解卷积的滚动轴承早期故障诊断方法。利用最佳参数相对应的最大相关峭度解卷积算法对原信号进行处理，得到解卷积信号后计算其包络谱，通过分析包络谱中幅值突出的频率成分来判断故障类型。早期故障仿真信号及实测全寿命数据分析结果表明，该方法可有效应用于轴承早期故障诊断。

Diagnosis Method for Rolling Bearing Incipient Faults Based on Sparsity of Envelope Spectrum and Maximum Correlated Kurtosis Deconvolution

Early fault  features of rolling  bearings  are   very weak and are  affected by environment  noise  seriously,so it  is difficult to draw fault features.Aiming at solving this problem,MCKD  was tried to diagnose faults  for bearings,and sparsity of envelope spectrum was used to select the optimal deconvolution period parameter,then incipient  fault diagnosis method for  rolling bearings was proposed  based on sparsity of envelope spectrum and MCKD.MCKD  method corresponding to  the optimal parameter was used to process the original signals  and the envelope spectrum of deconvolution signals was obtained,the bearing faults were  judged by analyzing the envelope spectrum.Simulated incipient fault signals and full lifetime datasets of  rolling bearings  were used  to  examine  the  feasibility  of this method and the results show  the new method can  be  applied  to  diagnose the incipient fault effectively.