Moment matching-based intraclass multisource domain adaptation network for bearing fault diagnosis

计算机科学 人工智能 分类器(UML) 模式识别(心理学) 匹配(统计) 概化理论 学习迁移 数据挖掘 机器学习 领域(数学分析) 断层(地质) 数学 统计 地质学 数学分析 地震学
作者
Yu Xia,Changqing Shen,Dong Wang,Yongjun Shen,Weiguo Huang,Zhongkui Zhu
出处
期刊:Mechanical Systems and Signal Processing [Elsevier BV]
卷期号:168: 108697-108697 被引量:80
标识
DOI:10.1016/j.ymssp.2021.108697
摘要

• A new multisource domain adaptation diagnosis method is proposed. • A moment distance metric is designed for multisource domain adaptation. • Conditional distribution distance is narrowed by an intraclass alignment training strategy. • The robustness is validated by case studies under different working conditions. Deep learning based fault diagnosis methods assume that training and testing data with sufficient labels are available and share a same distribution. In practical scenarios, this assumption does not generally hold due to variable working conditions of rotating machineries and the difficulty in labeling vibration data under all working conditions. Transfer learning (TL) overcomes this problem by utilizing knowledge learned from the source domain to help accomplish tasks on the target domain. Although TL based fault diagnosis has been considerably studied, most studies mainly focus on single-source TL. Since multisource domains with labeled samples from which more useful knowledge can be extracted are available, in this paper, a novel multisource TL model, called the moment matching-based intraclass multisource domain adaptation network, is proposed. This model uses a feature learner to generate features of each source and target domain data to enable the joint weight classifier to predict target labels. It also introduces a moment matching-based distance metric to reduce the distance among all source domains and the target domain. During the training of the model, an intraclass alignment training strategy is applied to match the marginal and conditional distributions of each domain simultaneously. Experiments on two datasets are performed, wherein the proposed method is used to identify bearing fault types under four load conditions. Experiment results, such as high diagnostic accuracies support the reliability and generalizability of the proposed model.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
6666发布了新的文献求助10
1秒前
无限雨南发布了新的文献求助10
1秒前
EgoElysia完成签到,获得积分10
1秒前
敏感雅香发布了新的文献求助10
2秒前
归尘发布了新的文献求助150
3秒前
zumri发布了新的文献求助10
3秒前
jia完成签到,获得积分10
5秒前
6秒前
6秒前
hino发布了新的文献求助10
6秒前
共享精神应助6666采纳,获得10
8秒前
shower_009完成签到,获得积分10
9秒前
11秒前
在水一方应助哈哈采纳,获得10
12秒前
12秒前
纯真追命完成签到 ,获得积分10
12秒前
12秒前
13秒前
咚咚锵完成签到,获得积分10
13秒前
13秒前
包容的琦发布了新的文献求助30
16秒前
梦里繁花发布了新的文献求助10
16秒前
Wang完成签到,获得积分10
18秒前
weilanhaian完成签到,获得积分10
18秒前
19秒前
蒋雪琴完成签到 ,获得积分10
19秒前
wjw发布了新的文献求助10
20秒前
21秒前
FashionBoy应助聪慧的正豪采纳,获得10
22秒前
22秒前
李长印发布了新的文献求助10
23秒前
23秒前
weilanhaian发布了新的文献求助10
24秒前
25秒前
nannan发布了新的文献求助10
25秒前
健忘小霜发布了新的文献求助10
25秒前
张大英完成签到 ,获得积分20
27秒前
27秒前
28秒前
29秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988868
求助须知:如何正确求助?哪些是违规求助? 3531255
关于积分的说明 11253071
捐赠科研通 3269858
什么是DOI,文献DOI怎么找? 1804822
邀请新用户注册赠送积分活动 881994
科研通“疑难数据库(出版商)”最低求助积分说明 809035