基于多通道循环平稳特征融合的智能诊断方法  

An Intelligent Diagnosis Method Based on Multi-channel Cyclostationary Features

作　　者：宋晓庆 张永鑫 SONG Xiaoqing;ZHANG Yongxin(School of Mechanical and Electrical Engineering,Zheng zhou Business University,Zhengzhou Henan 451200,China)

机构地区：[1]郑州商学院信息与机电工程学院,河南郑州451200

出　　处：《机床与液压》2023年第16期222-228,共7页Machine Tool & Hydraulics

摘　　要：同一监测点的多源通道信号可以为精准故障诊断提供更全面的特征信息,然而多源特征的有效融合仍然具有挑战性。为解决此问题,耦合隐马尔科夫(CHMM)被用来有效融合双通道信息的循环平稳特征,即用快速谱相关(FSC)提取特征,从而为提高滚动轴承智能诊断正确率提供有效多源融合特征向量支撑。FSC分析方法用于滚动轴承同源双通道振动信号的特征提取;参数优化选取后的CHMM对双通道同源特征进行融合,实现滚动轴承的智能诊断。通过滚动轴承常规故障实验和全寿命加速疲劳实验,验证了所述方法不仅能用于滚动轴承故障的智能分类,而且还能用于滚动轴承的有效性能退化评估。此外,通过对比研究验证了所述方法的优越性。The multi-source channel signals of the same monitoring point can provide more comprehensive feature information for accurate fault diagnosis,but the effective fusion of multi-source features is still challenging.To solve this problem,the coupled hidden Markov model(CHMM)was used to fuse the cyclostationary features of the dual-channel information effectively,that was,the features were extracted by fast spectral correlation(FSC),so as to provide effective multi-source fusion feature vector support for improving the accuracy of intelligent diagnosis of rolling bearings.The FSC analysis method was used to extract the features of the homologous dual-channel vibration signals of rolling bearings.The CHMM selected through parameter optimization was used to fuse the dual-channel homologous features to realize the intelligent diagnosis of rolling bearings.Through rolling bearing conventional fault experiments and full-life accelerated fatigue experiments,it is verified that the method can not only be used for intelligent classification of rolling bearing faults,but also for effective performance degradation evaluation of rolling bearings.Furthermore,the superiority of the described method is verified by comparative study.

关 键 词：耦合隐马尔科夫 循环平稳 多源特征 快速谱相关 智能诊断 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置]