Volume 36 Issue 9
Sep.  2021
Turn off MathJax
Article Contents
Article Navigation >  Journal of Aerospace Power  > 2021  >  36(9): 1793-1803
LIANG Haitao, WANG Ligang, WANG Liang, ZHANG Qingfeng. Bearing fault diagnosis based on ARCN model[J]. Journal of Aerospace Power, 2021, 36(9): 1793-1803. doi: 10.13224/j.cnki.jasp.20210186
Citation: LIANG Haitao, WANG Ligang, WANG Liang, ZHANG Qingfeng. Bearing fault diagnosis based on ARCN model[J]. Journal of Aerospace Power, 2021, 36(9): 1793-1803. doi: 10.13224/j.cnki.jasp.20210186
shu

Bearing fault diagnosis based on ARCN model

doi: 10.13224/j.cnki.jasp.20210186
  • 1.

    Mechanical Engineering Department， Sichuan Tri-star General Aviation Company Limited，Chengdu 610051，China

  • 2.

    Guanghan Flight College， Civil Aviation Flight University of China，Guanghan 618307，China

  • 3.

    Flight Technology College， Civil Aviation Flight University of China，Guanghan 618307，China

  • Received Date: 2021-04-21
  • Publish Date: 2021-09-28
    Abstract
  • The attention recurrent and capsule network （ARCN） diagnosis model was proposed by integrating the attention cycle mechanism and capsule network.Firstly，the bidirectional LSTM network was used to extract the time-series characteristic information to construct the primary capsule.Secondly，routing mechanism and attention cycle mechanism were used to construct adaptively digital capsule.The accuracy，robustness，stability and convergence error of ARCN model in bearing fault identification were verified by bearing experiment data of Western Reserve University.The accuracy of ARCN model was 1.2% higher than that of Caps model.The convergence error of the ARCN model reached 0.2.Based on the experimental simulation platform，the vibration signals of normal，inner ring fault，outer ring fault and rolling element fault were collected.The results showed that the misdiagnosis probability of each kind of fault was less than 1% of the total samples under ARCN model.

     

    • FullText(HTML)
  • loading
    • References(18)
  • [1]
    马圣.极限学习机在航空轴承故障诊断中的应用研究[D].天津：中国民航大学,2019.
    [2]
    蔡赛男,宋卫星,班利明,等.基于鲸鱼算法优化LSSVM的滚动轴承故障诊断[EB/OL].[2021-04-17].https:∥doi.org/10.13195/j.kzyjc.2020.1147.
    [3]
    宋霖,宿磊,李可,等.基于SSD和1DCNN的滚动轴承故障诊断方法[J].华中科技大学学报（自然科学版）,2020,48（12）:38-43.
    [4]
    李红贤,韩延,吴敬涛,等.基于ICA包络增强MEMD的滚动轴承故障诊断[J].航空动力学报,2021,36（2）:405-412.
    [5]
    皮骏,马圣,贺嘉诚,等.基于IGA-ELM网络的滚动轴承故障诊断[J].航空学报,2018,39（9）:233-244.
    [6]
    HAIDONG S,HONGKAI J,YING L,et al.A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders[J].Mechanical Systems and Signal Processing,2018,102（1）:278-297.
    [7]
    宫文峰,陈辉,张泽辉,等.基于改进卷积神经网络的滚动轴承智能故障诊断研究[J].振动工程学报,2020,33（2）:400-413.
    [8]
    ZHU Z A,PENG G A,CHEN Y A,et al.A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis[J].Neurocomputing,2019,323:62-75.
    [9]
    ZHANG S W,ZHANG S B,ZHANG C L,et al.Cucumber leaf disease identification with global pooling dilated convolutional neural network[J].Computers and Electronics in Agriculture,2019,162:422-430.
    [10]
    SABOUR S,FROSST N,HINTON G E.Dynamic routing between capsules[R].Long Beach,US：the 31st International Conference on Neural Information Processing Systems,2017.
    [11]
    MADHU G,GOVARDHAN A,SRINIVAS B S,et al.Imperative dynamic routing between capsules network for malaria classification[J].Computers,Materials and Continua,2021,680（1）：903-919.
    [12]
    CHEN T Y,WANG Z H.,YANG X,et al.A deep capsule neural network with stochastic delta rule for bearing fault diagnosis on raw vibration signals[J].Measurement,2019,148:106857.1-106857.15.
    [13]
    衷路生,刘东东.多级神经网络的轴承故障诊断研究[J].计算机工程与应用,2020,56（7）:193-199.
    [14]
    RU Y,HUANG,YI X,et al.Deep decoupling convolutional neural network for intelligent compound fault diagnosis[J].IEEE Access,2018，7:1848-1858.
    [15]
    沈炜域.基于自注意力与动态路由的文本建模方法[J].软件导刊,2019,18（1）:56-60,64.
    [16]
    张文涛.基于BiLSTM网络的领域问答系统关键技术研究[D].长沙：湖南师范大学,2020.
    [17]
    Case Western Reserve University Bearing Data Center.Bearing data center fault test data[EB/OL].[2020- 10-01].http:∥www.eecs.case.edu/laboratory/bearing.
    [18]
    朱娜,母其海,刘志邦,等.基于多尺度小波变换和时频图的微波源故障检测[J].电子测量技术,2021,44（13）:48-53.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF(1597.0KB)
    XML

    Article Metrics

    Article views (387) PDF downloads(189) Cited by()
    Proportional views
    Related

    Copyright © 《Journal of Aerospace Power》 All Rights Reserved.

    Supported by: Beijing Renhe Information Technology Co., Ltd.  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return