基于循环自相关和多域核极限学习机的滚动轴承故障识别方法

ROLLER BEARING FAULT RECOGNITION METHOD BASED ON CYCLIC AUTOCORRELATION AND MULTI-DOMAIN KERNEL LIMIT LEARNING MACHINE

根据轴承信号特点将二阶循环解调信息引入到机器学习中,提出一种基于循环自相关(CAF)频域特征和时域特征(TD)相结合的多域核极限学习机(MKELM)以精确识别轴承状态。该算法针对轴承信号的二阶循环特性构建CAF函数提取样本的循环频域特征,并与样本时域特征量结合;在此同时设计多域特征向量的匹配因子融合TD与CAF特征向量;最后将融合后的CAF-TD样本特征输入到核极限学习机中进行聚类回归。主轴轴承实验结果表明:基于CAF提取的循环频域统计量能够敏感的反映信号特征,与经典分类器比较,CAF-TD多域核极限学习机可以抽取有限样本更多特征信息,获得更准确的诊断结果。

According to characteristics of the bearing signal,the second-order cyclic demodulation information was introduced into machine learning,and a multi-domain kernel extreme learning machine(MKELM) based on the combination of cyclic autocorrelation(CAF) frequency domain features and time domain features(TD) was proposed to accurately identify the bearing status.The algorithm constructed a CAF function based on the second-order cyclic characteristics of the bearing signal to extract the cyclic frequency domain features of the samples,then combined them with the time domain feature quantities of the samples.The matching factors of multi-domain feature vectors was designed to fuse TD and CAF feature vectors;finally,the fused CAF-TD sample features was input into the kernel extreme learning machine for cluster regression.The spindle bearing experimental results show that the cyclic frequency domain statistics extracted based on CAF can sensitively reflect the signal characteristics.Compared with the classic classifier,the CAF-TD multi-domain kernel extreme learning machine can extract more feature information from limited samples and obtain more accurate diagnostic result.