摘要
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。
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 pro-posed based on sparsity of envelope spectrum and MCKD.MCKD method corresponding to the opti-mal 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 in-cipient 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 effec-tively.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2015年第11期1450-1456,共7页
China Mechanical Engineering
基金
中央高校基本科研业务费专项资金资助项目(13QN49)
河北省自然科学基金资助项目(E2014502052)
关键词
滚动轴承
稀疏度
最大相关峭度解卷积
故障诊断
rolling bearing
sparsity
maximum correlated kurtosis deconvolution（MCKD）
fault diagnosis
