沈阳航空航天大学学报2024，Vol.41Issue(1)：36-44,9.DOI:10.3969/j.issn.2095-1248.2024.01.005
基于联合指标的滚动轴承振动信号重构及故障诊断
Rolling bearing vibration signal reconstruction based on joint indicators and fault diagnosis
摘要
Abstract
In order to improve the effectiveness of feature extraction and accuracy of fault identifica-tion of rolling bearing,a signal reconstruction method based on joint indicators and a fault diagnosis method based on CWT-2DCNN were proposed.First,a joint indicator was constructed according to kurtosis and cross-correlation number to screen and reconstruct the intrinsic mode fuction(IMF)com-ponents obtained by ensemble empirical mode decomposition(EEMD).Secondly,continue wavelet transform(CWT)was used to extract the features of the reconstructed signal in time-frequency domain.Finally,a fault recognition model based on convolutional neural network(CNN)was constructed with time-frequency feature diagram as input,so as to realize the intelligent fault diagnosis of rolling bear-ing.The experimental results show that the fault diagnosis accuracy of the proposed signal reconstruc-tion and fault diagnosis method is 99.48%,and it still has a high correct recognition rate under strong noise,indicating that it has a strong generalization ability.关键词
峭度/互相关系数/卷积神经网络/时频特征/故障诊断/信号重构/滚动轴承/联合指标Key words
kurtosis/cross-correlation number/convolutional neural network/time-frequency char-acteristics/fault diagnosis/signal reconstruction/rolling bearing/joint indicators分类
机械制造引用本文复制引用
高铭悦,蒋丽英,张群晨,张瀛予,李贺..基于联合指标的滚动轴承振动信号重构及故障诊断[J].沈阳航空航天大学学报,2024,41(1):36-44,9.基金项目
国家自然科学基金(项目编号:62003223). （项目编号:62003223）
