Prototype-assisted multiscale graph representation learning-based mechanical fault detection method under complex operating conditions

故障检测与隔离 自编码 计算机科学 提取器 图形 代表(政治) 异常检测 人工智能 特征学习 模式识别(心理学) 无监督学习 特征(语言学) 数据挖掘 深度学习 工程类 理论计算机科学 语言学 哲学 工艺工程 政治 法学 政治学 执行机构
作者
Wei Xiang,Shujie Liu,Hongkun Li,Chen Yang,Shunxin Cao,Kongliang Zhang
出处
期刊:Structural Health Monitoring-an International Journal [SAGE Publishing]
标识
DOI:10.1177/14759217241291268
摘要

Effective anomaly detection and timely fault warning are essential to ensure the continuous and safe operation of mechanical equipment and to prevent equipment deterioration. In the unsupervised modeling and detection scenario, fault detection methods based on the autoencoder framework have been widely concerned and applied. Unfortunately, such methods can only be applied to specific or constant operating conditions, and their detection performance is greatly reduced due to the different data distribution in the face of complex operating conditions. Aiming at the problem of unsupervised fault detection under complex operating conditions, this article proposes a prototype-assisted multiscale graph representation learning-based mechanical fault detection method. First, the vibration data of the equipment is fed into the multiscale decomposition module (MDM) to obtain multiscale feature maps that can express rich detail information. Then, the multiscale feature maps are fed into the graph representation learning module (GRLM) to fully learn the potential relationships and interactions between different scales and provide a more comprehensive representation of the dynamic characteristics of the equipment. Finally, multiple MDMs and GRLMs are cascaded to construct a feature extractor to map the data of each operating condition to the latent space, and the proposed prototype-assisted strategy is used to determine the real-time state of the equipment. Case studies have been carried out on two different pieces of mechanical equipment. The experimental results show that the average accuracy of the proposed method is as high as 98.44% and 98.90%, respectively, and it maintains a low missed detection rate and zero false alarm rate in the two validation processes, which is more in line with the needs of engineering applications than other comparison methods.
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
量子星尘发布了新的文献求助10
8秒前
CyberHamster完成签到,获得积分10
18秒前
xiaohong完成签到,获得积分10
21秒前
朱比特完成签到,获得积分10
22秒前
23秒前
zmuzhang2019发布了新的文献求助10
29秒前
onestepcloser完成签到 ,获得积分0
29秒前
zoe完成签到 ,获得积分10
30秒前
发嗲的慕蕊完成签到 ,获得积分10
31秒前
Linson完成签到,获得积分10
32秒前
顾矜应助赵三岁采纳,获得10
46秒前
yyy2025完成签到,获得积分10
50秒前
木雨亦潇潇完成签到,获得积分10
57秒前
香蕉觅云应助nine2652采纳,获得10
59秒前
量子星尘发布了新的文献求助10
1分钟前
芳华如梦完成签到 ,获得积分10
1分钟前
1分钟前
1分钟前
1分钟前
土豆丝完成签到 ,获得积分10
1分钟前
琦琦完成签到,获得积分10
1分钟前
zzzz完成签到,获得积分20
1分钟前
GEZIKU完成签到 ,获得积分10
1分钟前
1分钟前
1分钟前
赵三岁发布了新的文献求助10
1分钟前
wwb完成签到,获得积分10
1分钟前
1分钟前
1分钟前
肯德基没有黄焖鸡完成签到 ,获得积分10
1分钟前
能干冰露完成签到,获得积分10
1分钟前
牛奶拌可乐完成签到 ,获得积分10
1分钟前
量子星尘发布了新的文献求助30
1分钟前
周小鱼完成签到 ,获得积分10
1分钟前
2分钟前
2分钟前
老张完成签到,获得积分10
2分钟前
2分钟前
zhugao完成签到,获得积分10
2分钟前
2分钟前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
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 4038039
求助须知:如何正确求助?哪些是违规求助? 3575756
关于积分的说明 11373782
捐赠科研通 3305574
什么是DOI,文献DOI怎么找? 1819239
邀请新用户注册赠送积分活动 892655
科研通“疑难数据库(出版商)”最低求助积分说明 815022