基于相空间重构和ICA-R的轴承故障特征增强方法

A Method of Enhancing the Fault Characteristics in Early Stage of Rolling Bearing Based on Phase-Space Reconstruction and ICA with Reference

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。

In the early stages of rolling bearing fault diagnosis, the characteristic frequencies of different parts are weak and difficult to identify. Thus, this paper presents a method to enhance the interested frequency components of rolling bearing. This method has better results and higher resistance to interference frequencies than do traditional methods using phase-space reconstruction to expand the time-domain signal into a high dimension space and independent component analysis with reference (ICA-R) to find the closest component to the reference signal. The simulation results and actual measurements of signals prove that this method is effective and feasible in early stage fault diagnosis, and also has certain values for engineering applications. © 2016, Editorial Department of JVMD. All right reserved.