Convolutional Neural Network Based Bearing Fault Diagnosis

被引：29
|
作者：
Duy-Tang Hoang [1 ]
Kang, Hee-Jun [2 ]
机构：
[1] Univ Ulsan, Grad Sch Elect Engn, Ulsan 680749, South Korea
[2] Univ Ulsan, Sch Elect Engn, Ulsan 680749, South Korea
来源：
INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2017, PT II | 2017年 / 10362卷
基金：
新加坡国家研究基金会;
关键词：
Convolutional neural network; Deep learning; Bearing fault diagnosis; Signal based fault diagnosis; FEATURES;
D O I：
10.1007/978-3-319-63312-1_9
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we propose a new bearing fault diagnosis method without the feature extraction, based on Convolutional Neural Network (CNN). The 1-D vibration signal is converted to 2-D data called vibration image. Then, the vibration images are fed into the CNN for bearing fault classification. Experiments are carried out with bearing data from the Case Western Reserve University Bearing Fault Database and its result are compared with the results of other methods to show the effectiveness of the proposed algorithm.
引用
收藏
页码：105 / 111
页数：7
相关论文
共 50 条
  • [41] Interpretable quadratic convolutional residual neural network for bearing fault diagnosis
    Zhiyong Luo
    Shuping Pan
    Xin Dong
    Xin Zhang
    Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2025, 47 (4)
  • [42] Multiscale Convolutional Neural Network With Feature Alignment for Bearing Fault Diagnosis
    Chen, Junbin
    Huang, Ruyi
    Zhao, Kun
    Wang, Wei
    Liu, Longcan
    Li, Weihua
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 70
  • [43] A novel convolutional neural network with global perception for bearing fault diagnosis
    Li, Xianguo
    Chen, Ying
    Liu, Yi
    Engineering Applications of Artificial Intelligence, 2025, 143
  • [44] Multiscale Residual Attention Convolutional Neural Network for Bearing Fault Diagnosis
    Jia, Linshan
    Chow, Tommy W. S.
    Wang, Yu
    Yuan, Yixuan
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 71
  • [45] Bearing intelligent fault diagnosis based on convolutional neural networks
    An, Jing
    An, Peng
    International Journal of Circuits, Systems and Signal Processing, 2022, 16 : 470 - 477
  • [46] A reinforcement neural architecture search convolutional neural network for rolling bearing fault diagnosis
    Li, Lintao
    Jiang, Hongkai
    Wang, Ruixin
    Yang, Qiao
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2023, 34 (11)
  • [47] Fault Diagnosis of Bearing Based on Convolutional Neural Network Using Multi- Domain Features
    Shao, Xiaorui
    Wang, Lijiang
    Kim, Chang Soo
    Ra, Ilkyeun
    KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2021, 15 (05): : 1610 - 1629
  • [48] Convolutional Neural Network Based on Spiral Arrangement of Features and Its Application in Bearing Fault Diagnosis
    Wang, Fengtao
    Deng, Gang
    Ma, Linjie
    Liu, Xiaofei
    Li, Hongkun
    IEEE ACCESS, 2019, 7 : 64092 - 64100
  • [49] Rolling Bearing Compound Fault Diagnosis Based on Parameter Optimization MCKD and Convolutional Neural Network
    Gao, Shuzhi
    Shi, Shuo
    Zhang, Yimin
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 71
  • [50] A rolling bearing fault diagnosis method based on a convolutional neural network with frequency attention mechanism
    Zhou, Hui
    Liu, Runda
    Li, Yaxin
    Wang, Jiacheng
    Xie, Suchao
    STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2024, 23 (04): : 2475 - 2495
12345