Bearing Fault Diagnosis Method Based on Adversarial Transfer Learning for Imbalanced Samples of Portal Crane Drive Motor

计算机科学 断层(地质) 方位(导航) 人工智能 对抗制 特征(语言学) 特征向量 模式识别(心理学) 工程类 语言学 哲学 地震学 地质学
作者
Yongsheng Yang,Zhongtao He,Haiqing Yao,Yifei Wang,Junkai Feng,Yuzhen Wu
出处
期刊:Actuators [Multidisciplinary Digital Publishing Institute]
卷期号:12 (12): 466-466
标识
DOI:10.3390/act12120466
摘要

Due to their unique structural design, portal cranes have been extensively utilized in bulk cargo and container terminals. The bearing fault of their drive motors is a critical issue that significantly impacts their operational efficiency. Moreover, the problem of imbalanced fault samples has a more pronounced influence on the application of novel fault diagnosis methods. To address this, the paper presents a new method called bidirectional gated recurrent domain adversarial transfer learning (BRDATL), specifically designed for imbalanced samples from portal cranes’ drive motor bearings. Initially, a bidirectional gated recurrent unit (Bi-GRU) is used as a feature extractor within the network to comprehensively extract features from both source and target domains. Building on this, a new Correlation Maximum Mean Discrepancy (CAMMD) method, integrating both Correlation Alignment (CORAL) and Maximum Mean Discrepancy (MMD), is proposed to guide the feature generator in providing domain-invariant features. Considering the real-time data characteristics of portal crane drive motor bearings, we adjusted the CWRU and XJTU-SY bearing datasets and conducted comparative experiments. The experimental results show that the accuracy of the proposed method is up to 99.5%, which is obviously higher than other methods. The presented fault diagnosis model provides a practical and theoretical framework for diagnosing faults in portal cranes’ field operation environments.
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
1秒前
粽粽完成签到,获得积分10
1秒前
夏惋清完成签到 ,获得积分0
1秒前
hhsong发布了新的文献求助10
1秒前
4秒前
4秒前
4秒前
Singularity应助清秀的怀蕊采纳,获得10
5秒前
5秒前
Niu发布了新的文献求助10
6秒前
几星霜发布了新的文献求助10
6秒前
小小完成签到,获得积分10
7秒前
深海鱼发布了新的文献求助30
8秒前
朱建军应助Ariesfei采纳,获得10
8秒前
鉴定为学计算学的完成签到,获得积分10
9秒前
xj305发布了新的文献求助10
10秒前
斯文败类应助勤奋的丸子采纳,获得10
10秒前
12秒前
辉辉发布了新的文献求助10
12秒前
13秒前
16秒前
海皇星空完成签到 ,获得积分10
16秒前
恒浚完成签到,获得积分10
17秒前
搞怪含巧完成签到,获得积分10
17秒前
王俞关注了科研通微信公众号
17秒前
热心不凡完成签到,获得积分10
18秒前
lqh完成签到,获得积分20
18秒前
20秒前
20秒前
20秒前
哈哈哈发布了新的文献求助10
20秒前
faye发布了新的文献求助10
21秒前
hhsong完成签到,获得积分10
21秒前
勤奋酒窝完成签到,获得积分10
23秒前
roser发布了新的文献求助10
23秒前
几星霜完成签到,获得积分10
24秒前
lin应助李子采纳,获得10
24秒前
Y垚发布了新的文献求助10
25秒前
zhige发布了新的文献求助30
26秒前
还不错的橙子完成签到,获得积分10
27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
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 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 3969557
求助须知:如何正确求助?哪些是违规求助? 3514377
关于积分的说明 11173836
捐赠科研通 3249692
什么是DOI,文献DOI怎么找? 1794979
邀请新用户注册赠送积分活动 875537
科研通“疑难数据库(出版商)”最低求助积分说明 804836