基于相空间重构和ICA-R的轴承故障特征增强方法

发布时间：2018-04-15 12:58

本文选题：特征增强 + 滚动轴承　；参考：《振动.测试与诊断》2016年06期

【摘要】：针对实测滚动轴承早期故障信号中故障特征频率成分微弱、难以识别及提取的问题,设计了一种结合相空间重构(phase-space reconstruction,简称PSR)和参考独立分量分析(independent component analysis with reference,简称ICA-R)的故障特征增强方法。利用相空间重构将一维时域信号拓展到高维,再进行参考独立分量分析,将所感兴趣的轴承故障特征频率成分进行增强。该方法相比传统频率提取方法具有效果好、对干扰频率抑制明显的特点。仿真结果和工程实测信号表明,该方法对滚动轴承早期故障特征提取有效可行,具有一定工程应用价值。
[Abstract]:Aiming at the problem that the fault characteristic frequency component is weak in the early fault signal of rolling bearing, it is difficult to identify and extract.A fault feature enhancement method combining phase space reconstruction phase-space construction (PSRs) with reference independent component analysis with reference (ICA-R) is proposed.The phase space reconstruction is used to extend the one dimensional time domain signal to high dimension, and then the reference independent component analysis (ICA) is carried out to enhance the frequency component of bearing fault features of interest.Compared with the traditional frequency extraction method, this method has the advantages of good effect and obvious suppression of interference frequency.The simulation results and the engineering measured signals show that this method is effective and feasible for the early fault feature extraction of rolling bearings and has certain engineering application value.
【作者单位】：重庆大学机械传动国家重点实验室;武汉锐科光纤激光器有限责任公司;
【基金】：国家自然科学基金资助项目(51675064,51475052) 中央高校基本科研业务费资助项目(106112016CDJIR115502)
【分类号】：TH133.3

【相似文献】