INTEGRATING EMPIRICAL MODE DECOMPOSITION AND CONVOLUTIONAL NEURAL NETWORK FOR EFFICIENT FAULT DIAGNOSIS IN METALLURGICAL MACHINERY

被引：0
|
作者：
Tang, X. F. [1 ]
机构：
[1] Univ Sci & Technol Liaoning, Sch Comp Sci & Software Engn, Anshan, Peoples R China
来源：
METALURGIJA | 2024年 / 63卷 / 3-4期
关键词：
metallurgical machinery; transmission; diagnosis of faults; intrinsic mode functions; convolutional neural networks;
D O I：
暂无
中图分类号：
TF [冶金工业];
学科分类号：
0806 ;
摘要：
The paper introduces an innovative framework for rotating machinery fault recognition by combining Empirical Mode Decomposition (EMD) and Convolutional Neural Network (CNN). This novel approach integrates feature extraction and selection, utilizing deep learning for precise classification of transmission components faults. Our method achieves an impressive accuracy of 98,97 %. This robust technology significantly enhances the detection and diagnosis of transmission faults in metallurgical plant, providing an efficient solution for intelligent manufacturing applications.
引用
收藏
页码：350 / 352
页数：3
相关论文
共 50 条
  • [1] Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition
    Xie, Yuan
    Zhang, Tao
    SHOCK AND VIBRATION, 2017, 2017
  • [2] Bearing fault diagnosis based on empirical mode decomposition and neural network
    Shao, Jiye
    Li, Jie
    Ma, Jiajun
    2015 8TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 1, 2015, : 118 - 121
  • [3] Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition
    Liu, Dong
    Lai, Xu
    Xiao, Zhihuai
    Hu, Xiao
    Zhang, Pei
    SHOCK AND VIBRATION, 2020, 2020
  • [4] A review on empirical mode decomposition in fault diagnosis of rotating machinery
    Lei, Yaguo
    Lin, Jing
    He, Zhengjia
    Zuo, Ming J.
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2013, 35 (1-2) : 108 - 126
  • [5] Applications of improved empirical mode decomposition in machinery fault diagnosis
    Ma, Wenpeng
    Zhang, Junhong
    Ma, Liang
    Liu, Yu
    Jia, Xiaojie
    Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis, 2015, 35 (04): : 637 - 644
  • [6] Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network
    Nishat Toma, Rafia
    Kim, Cheol-Hong
    Kim, Jong-Myon
    ELECTRONICS, 2021, 10 (11)
  • [7] Structural fault diagnosis using empirical mode decomposition and artificial neural network
    Cheng, Ling
    Chen, Qian
    Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis, 2010, 30 (02): : 189 - 192
  • [8] Fault diagnosis of power grid based on variational mode decomposition and convolutional neural network
    Zhang, Qian
    Ma, Wenhao
    Li, Guoli
    Ding, Jinjin
    Xie, Min
    ELECTRIC POWER SYSTEMS RESEARCH, 2022, 208
  • [9] Early fault diagnosis of rotating machinery based on wavelet packets-Empirical mode decomposition feature extraction and neural network
    Bin, G. F.
    Gao, J. J.
    Li, X. J.
    Dhillon, B. S.
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2012, 27 : 696 - 711
  • [10] Using Convolutional Neural Network for Vibration Fault Diagnosis Monitoring in Machinery
    Yeh, Chiao Wei
    Chen, Rongshun
    PROCEEDINGS OF THE 2018 IEEE INTERNATIONAL CONFERENCE ON ADVANCED MANUFACTURING (IEEE ICAM), 2018, : 246 - 249
12345