亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Wasserstein bi-classifier adversarial learning network for machinery fault diagnostics

对抗制 分类器(UML) 人工智能 计算机科学 机器学习
作者
Yalun Fan,Liang Guo,Yaoxiang Yu,Yi Sun,Tao Luo,Kexin Hou,Weilin Li,Hongli Gao
出处
期刊:Structural Health Monitoring-an International Journal [SAGE Publishing]
标识
DOI:10.1177/14759217241266893
摘要

In industrial applications of machinery fault diagnostics, deep learning has been widely adopted to process large amounts of monitoring data. Unfortunately, due to the domain discrepancy, diagnostic models trained with source domain data suffer from degraded diagnostic performance on the target domain. To address this problem, a Wasserstein bi-classifier adversarial learning network (WBALN) is proposed. Specifically, WBALN consists of a feature extractor, two classifiers, and a discrepancy metric based on Wasserstein distance. A two-stream optimization strategy is used in the training process, which involves jointly performing bi-classifier adversarial learning and Wasserstein generative adversarial network (WGAN)-based adversarial learning. In the bi-classifier training stream, a min-max game is conducted between a discrepancy detector composed of two classifiers and the feature extractor to reduce the disparity between these classifiers. In the WGAN-based training stream, a Wasserstein adversarial discrepancy (WAD) is applied in combination with the original classifier as a domain discriminator, which achieves fault diagnosis and distribution alignment through a unified objective. This WAD enables WBALN to achieve sufficient feature alignment using the predicted discriminative information. In addition, the utilization of the nuclear-norm is useful for ensuring the determinacy and diversity of predictions. Except for ordinary domain adaptation, WBALN is also extended for challenging problems about inter-class imbalanced domain adaptation. The performance of the proposed WBALN is verified through multiple experiments on two bearing datasets.
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
科研通AI2S应助科研通管家采纳,获得10
21秒前
科研通AI2S应助科研通管家采纳,获得10
21秒前
ICSSCI完成签到,获得积分10
26秒前
39秒前
董可以发布了新的文献求助10
43秒前
风华正茂完成签到,获得积分10
1分钟前
1分钟前
1分钟前
jimmy_bytheway完成签到,获得积分0
1分钟前
桃桃发布了新的文献求助10
1分钟前
可爱的函函应助桃桃采纳,获得10
1分钟前
1分钟前
2分钟前
2分钟前
2分钟前
NexusExplorer应助科研通管家采纳,获得10
2分钟前
所所应助爱笑的毛衣采纳,获得10
2分钟前
2分钟前
2分钟前
duan完成签到 ,获得积分10
2分钟前
holder完成签到,获得积分10
3分钟前
3分钟前
沐白发布了新的文献求助10
3分钟前
3分钟前
刘宇童发布了新的文献求助10
3分钟前
大模型应助吕易巧采纳,获得10
3分钟前
迷人问兰完成签到,获得积分10
3分钟前
闪闪映易完成签到 ,获得积分10
3分钟前
3分钟前
吕易巧发布了新的文献求助10
3分钟前
吕易巧完成签到,获得积分10
3分钟前
4分钟前
Liiiiiiiiii发布了新的文献求助10
4分钟前
XuchaoD完成签到,获得积分10
4分钟前
4分钟前
今后应助Liiiiiiiiii采纳,获得10
4分钟前
顾矜应助科研通管家采纳,获得10
4分钟前
Akim应助科研通管家采纳,获得10
4分钟前
4分钟前
4分钟前
高分求助中
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
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 3990049
求助须知:如何正确求助?哪些是违规求助? 3532108
关于积分的说明 11256354
捐赠科研通 3270976
什么是DOI,文献DOI怎么找? 1805166
邀请新用户注册赠送积分活动 882270
科研通“疑难数据库(出版商)”最低求助积分说明 809228