Automatic Transmission Bearing Fault Diagnosis Based on Comprehensive Index Method and Convolutional Neural Network

被引:3
|
作者
Li, Guangxin [1 ]
Chen, Yong [1 ]
Wang, Wenqing [2 ]
Wu, Yimin [1 ]
Liu, Rui [1 ]
机构
[1] Hebei Univ Technol, Sch Mech Engn, Tianjin Key Lab Power Transmiss & Safety Technol, Tianjin 300130, Peoples R China
[2] Weichai Power Co Ltd, Weifang 261061, Peoples R China
来源
WORLD ELECTRIC VEHICLE JOURNAL | 2022年 / 13卷 / 10期
基金
国家重点研发计划;
关键词
rolling-element bearing; complete ensemble empirical mode decomposition with adaptive noise; independent component analysis; convolutional neural network; fault diagnosis;
D O I
10.3390/wevj13100184
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Rolling-element bearing fault diagnosis has some problems in the applied environment, such as low signal-to-noise ratio, weak feature extraction, low efficiency of feature learning and the complex structure of diagnosis models. A fault diagnosis method based on the comprehensive index method, complete ensemble empirical mode decomposition with adaptive noise independent component analysis (CEEMDANICA) and two-dimensional convolutional neural network (TDCNN) is proposed. Firstly, the original vibration signal of the bearing is preprocessed by CEEMDANICA, and the ICA components with different frequencies are obtained. Secondly, the ICA components are selected as the sample set by using multiscale permutation entropy, correlation coefficient, kurtosis and box dimension. Finally, the sample set are trained and tested by a DCNN model to realize the fault diagnosis of different bearing fault types. In order to verify the reliability of the method, a bearing fault vibration monitoring platform for an electric vehicle two-speed automatic transmission was built to collect the bearing vibration signals of multiple fault types under different working conditions. The diagnostic accuracy of several deep learning models is compared. The results show that the proposed method can realize the single and compound fault diagnosis of rolling-element bearings in an automatic transmission, with a high degree of accuracy.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] A Fault Diagnosis Method of Rolling Bearing Based on Convolutional Neural Network
    Zhang, Bangcheng
    Gao, Shuo
    Hu, Guanyu
    Gao, Zhi
    Zhao, Yadong
    Du, Jianzhuang
    [J]. 2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2023, : 4709 - 4713
  • [2] Convolutional Neural Network Based Bearing Fault Diagnosis
    Duy-Tang Hoang
    Kang, Hee-Jun
    [J]. INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2017, PT II, 2017, 10362 : 105 - 111
  • [3] Rolling Bearing Composite Fault Diagnosis Method Based on Convolutional Neural Network
    Chen, Song
    Guo, Dong-ting
    Chen, Li-ai
    Wang, Da-gui
    [J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2024, 38 (03)
  • [4] Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph
    Li, Zhibo
    Li, Yuanyuan
    Sun, Qichun
    Qi, Bowei
    [J]. ENTROPY, 2022, 24 (11)
  • [5] Rolling bearing fault convolutional neural network diagnosis method based on casing signal
    Zhang, Xiangyang
    Chen, Guo
    Hao, Tengfei
    He, Zhiyuan
    [J]. JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2020, 34 (06) : 2307 - 2316
  • [6] Rolling bearing fault convolutional neural network diagnosis method based on casing signal
    Xiangyang Zhang
    Guo Chen
    Tengfei Hao
    Zhiyuan He
    [J]. Journal of Mechanical Science and Technology, 2020, 34 : 2307 - 2316
  • [7] A fault diagnosis method based on improved parallel convolutional neural network for rolling bearing
    Xu, Tao
    Lv, Huan
    Lin, Shoujin
    Tan, Haihui
    Zhang, Qing
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING, 2023, 237 (12) : 2759 - 2771
  • [8] Convolutional neural network diagnosis method of rolling bearing fault based on casing signal
    Zhang, Xiangyang
    Chen, Guo
    Hao, Tengfei
    He, Zhiyuan
    Li, Xujin
    Cheng, Zhenjie
    [J]. Hangkong Dongli Xuebao/Journal of Aerospace Power, 2019, 34 (12): : 2729 - 2737
  • [9] A Novel Method for Bearing Fault Diagnosis Based on a Parallel Deep Convolutional Neural Network
    Lin, Zhuonan
    Wang, Yongxing
    Guo, Yining
    Tong, Xiangrui
    Wei, Fanrong
    Tong, Ning
    [J]. SYMMETRY-BASEL, 2024, 16 (04):
  • [10] An Analysis Method for Interpretability of Convolutional Neural Network in Bearing Fault Diagnosis
    Guo, Liang
    Gu, Xi
    Yu, Yaoxiang
    Duan, Andongzhe
    Gao, Hongli
    [J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2024, 73 : 1 - 12