摘要
针对实际应用中智能轴承故障诊断面临标签样本严重不足的问题,提出一种结合压缩感知、字典学习和迁移的数据增强算法,用于小样本故障诊断研究。首先,利用源域标签数据通过小波包字典学习和优化方法生成特定源域字典,并得到共享表示系数,获取故障内在信息;之后采用少量目标域信号微调共享表示系数,并更新源域字典生成迁移字典;最后通过共享表示系数和迁移字典生成大量具有目标域特征的新样本,实现数据增强。采用常用的深度故障诊断网络对该数据增强算法进行了诊断性能验证,结果表明该方法产生的信号具有故障的有效信息,用于模型训练和识别能够取得较好的诊断性能。该方法为小样本故障诊断问题提出了新的思路。
Here,aiming at the problem of serious shortage of label samples for intelligent bearing fault diagnosis in practical applications,a data augmentation algorithm combining compressed sensing,dictionary learning and transfer was proposed for small sample bearing fault diagnosis study.Firstly,source domain label data were used to generate a specific source domain dictionary through wavelet packet dictionary learning and optimization method,obtain shared representation coefficients and acquire intrinsic fault information.Afterwards,a small amount of target domain signals was used to fine-tune shared representation coefficients,update the source domain dictionary and generate a transfer dictionary.Finally,a large number of new samples with target domain features were generated through shared representation coefficients and the transfer dictionary to realize data augmentation.The commonly used deep fault diagnosis network was used to verify the diagnostic performance of the proposed data augmentation algorithm here.The results showed that signals generated with the proposed method have effective information of faults,and can achieve better diagnostic performance when used for model training and recognition;the proposed method can provide a new approach for small sample fault diagnosis problems.
作者
孙洁娣
赵彬集
温江涛
时培明
SUN Jiedi;ZHAO Binji;WEN Jiangtao;SHI Peiming(School of Information Science and Engineering,Yanshan University,Qinhuangdao 066004,China;Hebei Provincial Key Lab of Information Transmission and Signal Processing,Yanshan University,Qinhuangdao 066004,China;Key Lab of Measurement Technology and Instrumentation of Hebei Province Yanshan University,Qinhuangdao 066004,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2024年第5期62-71,120,共11页
Journal of Vibration and Shock
基金
国家自然科学基金(61973262)
河北省自然科学基金资助项目(E2020203061)
河北省重点实验室项目(202250701010046)。
关键词
轴承故障诊断
数据增强
压缩感知重构
字典迁移
fault diagnosis
data augmentation
compressed sensing reconstruction
dictionary transfer
作者简介
第一作者:孙洁娣,女,博士,教授,1975年生。
