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的填写文献信息,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!

Dynamic Graph-Based Feature Learning With Few Edges Considering Noisy Samples for Rotating Machinery Fault Diagnosis

图形 计算机科学 模式识别(心理学) 算法 人工智能 理论计算机科学
作者
Kaibo Zhou,Chaoying Yang,Jie Liu,Qi Xu
出处
期刊:IEEE Transactions on Industrial Electronics [Institute of Electrical and Electronics Engineers]
卷期号:69 (10): 10595-10604 被引量:60
标识
DOI:10.1109/tie.2021.3121748
摘要

Due to its ability to learn the relationship among nodes from graph data, the graph convolution network (GCN) has received extensive attention. In the machine fault diagnosis field, it needs to construct input graphs reflecting features and relationships of the monitoring signals. Thus, the quality of the input graph affects the diagnostic performance. But it still has two limitations: 1) the constructed input graph usually has redundant edges, consuming excessive computational costs; 2) the constructed input graph cannot reflect the relationship between the noisy signals well. In order to overcome them, a dynamic graph-based feature learning with few edges considering noisy samples is proposed for rotating machinery fault diagnosis in this article. Noisy vibration signals are converted into one spectrum feature-based static graph, where redundant edges are simplified by the distance metric function. Edge connections of the input static graph are updated according to the relationship among high-level features extracted by the GCN. Based on this, dynamic input graphs are reconstructed as new graph representations for noisy samples. To verify the effectiveness of the proposed method, validation experiments were conducted on practical platforms, and results show that the dynamic input graph with few edges can effectively improve the diagnostic performance under different SNRs.
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
大幅提高文件上传限制,最高150M (2024-4-1)

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
冰激凌完成签到,获得积分10
20秒前
归海梦岚完成签到,获得积分0
25秒前
29秒前
36秒前
43秒前
悠悠发布了新的文献求助10
50秒前
邹醉蓝完成签到,获得积分10
54秒前
634301059完成签到 ,获得积分10
58秒前
悠悠完成签到,获得积分20
1分钟前
吃不饱星球球长应助jason采纳,获得10
1分钟前
1分钟前
李爱国应助悠悠采纳,获得10
1分钟前
1分钟前
充电宝应助chiyudoubao采纳,获得10
1分钟前
Lucas应助gulmira采纳,获得10
1分钟前
wanci应助Dr.Leon采纳,获得10
1分钟前
1分钟前
单纯的雅香完成签到,获得积分10
2分钟前
2分钟前
wangyang完成签到,获得积分10
2分钟前
南宫雪完成签到 ,获得积分10
2分钟前
无私航空发布了新的文献求助50
2分钟前
lixuebin完成签到 ,获得积分10
2分钟前
wangyang发布了新的文献求助10
2分钟前
2分钟前
无私航空完成签到,获得积分10
2分钟前
2分钟前
gulmira发布了新的文献求助10
2分钟前
2分钟前
2分钟前
可爱的你发布了新的文献求助60
2分钟前
WW应助gulmira采纳,获得10
3分钟前
3分钟前
3分钟前
jason完成签到,获得积分10
3分钟前
3分钟前
jason发布了新的文献求助10
3分钟前
chiyudoubao发布了新的文献求助10
3分钟前
可爱的你完成签到,获得积分10
3分钟前
3分钟前
高分求助中
Sustainability in Tides Chemistry 1500
Handbook of the Mammals of the World – Volume 3: Primates 805
拟南芥模式识别受体参与调控抗病蛋白介导的ETI免疫反应的机制研究 550
Gerard de Lairesse : an artist between stage and studio 500
Digging and Dealing in Eighteenth-Century Rome 500
Queer Politics in Times of New Authoritarianisms: Popular Culture in South Asia 500
Manual of Sewer Condition Classification 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 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 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3068019
求助须知:如何正确求助?哪些是违规求助? 2722010
关于积分的说明 7475939
捐赠科研通 2369097
什么是DOI,文献DOI怎么找? 1256116
科研通“疑难数据库(出版商)”最低求助积分说明 609454
版权声明 596795