Machinery Fault Diagnosis Based on Domain Adaptation to Bridge the Gap Between Simulation and Measured Signals

鉴别器 断层(地质) 有限元法 卷积神经网络 人工神经网络 计算机科学 方位(导航) 桥(图论) 领域(数学分析) 滚动轴承 故障模拟器 人工智能 工程类 模式识别(心理学) 陷入故障 故障检测与隔离 执行机构 结构工程 振动 声学 数学 数学分析 地震学 地质学 内科学 物理 探测器 电信 医学
作者
Yunxia Lou,Anil Kumar,Jiawei Xiang
出处
期刊:IEEE Transactions on Instrumentation and Measurement [Institute of Electrical and Electronics Engineers]
卷期号:71: 1-9 被引量:85
标识
DOI:10.1109/tim.2022.3180416
摘要

In intelligent fault diagnosis, the success of artificial intelligence (AI) models is highly dependent on labeled training samples, which may not be obtained in real-world applications. Recently, a finite element method (FEM) simulation-based personalized diagnosis method was developed to overcome the problems of insufficient and incomplete labeled training samples. However, the simulation signals obtained using the FEM and measured signals actually have a certain deviation. To supplement the FEM simulation-based personalized diagnosis method, a fault diagnosis method using domain adaptation (DA) is proposed to bridge the gap between simulation signals and measured signals. First, the FEM is adopted to obtain sufficient and complete simulation samples of all the fault categories as the original fault samples in the source domain. Second, the original simulation fault samples are adjusted using a generative adversarial network (GAN)-based DA network to make them similar to the measured samples through the adversarial training of the refiner and domain discriminator. Last, credible adjustment fault samples and measured fault samples obtained in machinery are applied to a convolutional neural network (CNN) for training and testing to complete the fault classification. The data obtained from rolling element bearing and gear test rigs are utilized to explore the feasibility of the proposed method, and the classification accuracies reach 99.44% and 99.58%, respectively. The comparison investigations using experimental data of gears and bearings indicate that the present method can accurately classify faults in machinery.
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
Lucas应助VitoLi采纳,获得10
刚刚
zsfxqq完成签到 ,获得积分10
1秒前
LHW完成签到,获得积分0
1秒前
若俗人完成签到,获得积分10
2秒前
拼搏一曲完成签到 ,获得积分10
2秒前
mito完成签到,获得积分10
3秒前
庾楼月宛如昨完成签到 ,获得积分10
5秒前
清脆如娆完成签到 ,获得积分10
7秒前
8秒前
高高小兔子完成签到,获得积分10
8秒前
aaronzhu1995完成签到,获得积分10
8秒前
nnnnn完成签到,获得积分10
9秒前
meng完成签到,获得积分10
9秒前
为你等候完成签到,获得积分10
10秒前
11秒前
萝卜卷心菜完成签到 ,获得积分10
11秒前
13秒前
这是对吧完成签到,获得积分10
14秒前
简单花花完成签到,获得积分10
15秒前
芙瑞完成签到 ,获得积分10
15秒前
惠_____完成签到 ,获得积分10
16秒前
16秒前
文安完成签到,获得积分10
17秒前
18秒前
萌萌许完成签到,获得积分10
20秒前
dlut0407完成签到,获得积分10
20秒前
22秒前
神秘玩家完成签到 ,获得积分10
23秒前
Yuuuu完成签到 ,获得积分10
24秒前
尔尔完成签到 ,获得积分10
24秒前
xixihaha完成签到,获得积分10
25秒前
小嚣张完成签到,获得积分10
27秒前
小豆豆严完成签到,获得积分10
27秒前
Glitter完成签到 ,获得积分10
28秒前
少女徐必成完成签到 ,获得积分10
28秒前
俍璟完成签到 ,获得积分10
29秒前
马騳骉完成签到,获得积分10
30秒前
YY完成签到,获得积分10
32秒前
33秒前
轻歌水越完成签到 ,获得积分10
35秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Immigrant Incorporation in East Asian Democracies 600
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
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 3968578
求助须知:如何正确求助?哪些是违规求助? 3513400
关于积分的说明 11167585
捐赠科研通 3248853
什么是DOI,文献DOI怎么找? 1794499
邀请新用户注册赠送积分活动 875131
科研通“疑难数据库(出版商)”最低求助积分说明 804664