摘要
提出一种空间迁移新思路,以提升齿轮箱故障诊断性能,其由辅助振动数据构成源领域、目标振动数据构成目标领域,迁移学习(Transfer learning,TL)将前者分类模型应用至后者,以克服短时间内目标振动数据不足的问题。根据频带选择独立成分分析(Band selective independent component analysis,BS‑ICA)规则选择迁移模型的数据领域,并提取其时域特征构成五维空间。利用均衡密度投影(Equilibrium density projection,MDP)将源领域和目标领域同时映射至二维投影空间,并最小化领域均值差异以拉近两者在低维空间内的距离。在投影空间内,利用逻辑回归(Logistic regression,LR)和支持向量机(Support vector machine,SVM)基分类器对映射样本实施分类。同时通过剔除低质量源领域样本,加入新采集样本以维持模型更新。利用Spectra Quest齿轮传动系统,对比迁移成分分析(Transfer composition analysis,TCA)、领域适应机(Domain selection machine,DSM)等传统TL模型,所提方法不但能提高工况快速变化时的诊断精度,同时能加快诊断速度,具有实际的工程应用价值。
To improve the performance of gearbox fault diagnosis,a space transfer strategy is proposed.Here the source domains are composed by multiple auxiliary channels and the target domain is composed by single target channel.With transfer learning(TL),the fault diagnosis models in the former can be applied in the latter to overcome the problem of lacking target data.Firstly,the domains are selected according to the band selective independent component analysis(BS-ICA)rule and the original five-dimension spaces are constructed by extracting their time-domain features.Secondly,the source and target domains are mapped to a public two-dimensional space using the equilibrium density projection(EDP).Meanwhile,the minimum mean difference strategy is used to minimize the difference between two projection spaces.Finally,the logistic regression(LR)and support vector machine(SVM)classifiers are both carried out for sample classification.Also,the diagnostic model can be updated by removing low-quality samples while adding high-quality samples in source domains.Based on the Spectra Quest’s gear drive system,the performance between proposed method and classical transfer strategies including transfer composition analysis(TCA)and domain selection machine(DSM)are compared,which indicates that the former has higher diagnostic accuracy as well as faster running speed when facing with rapid change of working conditions,thus possessing high value in application of engineering.
作者
沈飞
陈超
徐佳文
严如强
SHEN Fei;CHEN Chao;XU Jia-wen;YAN Ru-qiang(School of Instrument Science and Engineering,Southeast University,Nanjing 210096,China;School of Mechanical Engineering,Xi′an Jiaotong University,Xi′an 710049,China)
出处
《振动工程学报》
EI
CSCD
北大核心
2021年第2期389-401,共13页
Journal of Vibration Engineering
基金
国家自然科学基金资助项目(51575102)
东南大学优秀博士学位论文培育基金资助项目(YBPY1887)。
关键词
故障诊断
齿轮箱
空间迁移
频带选择独立成分分析
均衡密度投影
fault diagnosis
gearbox
space transfer
band selective independent component analysis
equilibrium density projection
