摘要
针对滚动轴承实际工作中缺少某种负载数据,使得源领域数据与目标领域数据属于不同分布,以及目标领域样本不含标签的问题,提出一种多域特征构建和无监督特征对齐的滚动轴承故障诊断方法。该方法利用变分模态分解结合奇异值分解获取振动信号的时频特征,再结合振动信号时域、频域特征构建多域特征集;引入迁移学习中能够实现无监督领域适应的子空间对齐(subspace alignment,SA)算法并进行改进,提出将核映射方法与SA算法相结合。将训练数据和测试数据映射到相同高维空间,在高维空间的子空间进行特征对齐,以增加数据类间区分性,实现不同负载下源领域特征向目标领域特征对齐。实验研究表明,与部分降维方法及无监督迁移学习方法相比,所提方法在目标领域无标签的情况下,能够利用滚动轴承已知负载数据识别出其他负载数据对应的状态,并具有较高的故障诊断准确率。
Aiming at the problem of data lack for rolling bearing under certain load in actual work,which causes great difference between source domain data and target domain data distribution,and the absence of labels in target domain samples,a fault diagnosis method of a rolling bearing was proposed based on multi-domain feature construction and unsupervised feature alignment.Variational mode decomposition and singular value decomposition were combined to obtain time-frequency features of vibration signals.And then combining time-domain and frequency-domain features of vibration signals to construct multi-domain feature sets.The subspace alignment(SA)algorithm which can realize unsupervised domain adaptation in transfer learning,was introduced and improved,and it was proposed to combine the kernel mapping method with SA algorithm.The training data and test data were mapping to the same high-dimensional space,and feature alignment was carried out in the subspace of the high-dimensional space to increase the discrimination between data class,and the alignment of source domain features to target domain features under different loads can be realized.The experimental results show that,compared with partial dimensionality reduction methods and unsupervised transfer learning methods,the proposed method can recognize the corresponding states of other load data using the known load data of rolling bearings without labels in the target domain,and has a higher fault diagnosis accuracy.
作者
康守强
邹佳悦
王玉静
谢金宝
V.I.MIKULOVICH
KANG Shouqiang;ZOU Jiayue;WANG Yujing;XIE Jinbao;V.I.MIKULOVICH(School of Electrical and Electronic Engineering,Harbin University of Science and Technology,Harbin 150080,Heilongjiang Province,China;Belarusian State University,Minsk 220030,Belarus)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2020年第1期274-281,共8页
Proceedings of the CSEE
基金
国家自然科学基金项目(51805120)
黑龙江省自然科学基金项目(LH2019E058)
黑龙江省本科高校青年创新人才培养计划(UNPYSCT-2017091)~~
关键词
故障诊断
迁移学习
无监督领域适应
滚动轴承
变负载
fault diagnosis
transfer learning
unsupervised domain adaptation
rolling bearing
varying loads
