Multi-Source Information Fusion Fault Diagnosis for Gearboxes Based on SDP and VGG

被引:7
|
作者
Fu, Yuan [1 ]
Chen, Xiang [1 ]
Liu, Yu [1 ]
Son, Chan [1 ,2 ]
Yang, Yan [1 ]
机构
[1] Chongqing Univ Technol, Dept Mech Engn, Chongqing 400054, Peoples R China
[2] Korea Elect Power Res Inst, 105 Munji Ro, Daejeon 34056, South Korea
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 13期
基金
中国国家自然科学基金;
关键词
SDP; VGG16; DS theory; fault diagnosis; gearbox gears;
D O I
10.3390/app12136323
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
A decision-level approach using multi-sensor-based symmetry dot pattern (SDP) analysis with a Visual Geometry Group 16 network (VGG16) fault diagnosis model for multi-source information fusion was proposed to realize accurate and comprehensive fault diagnosis of gearbox gear teeth. Firstly, the SDP technique was used to perform a feature-level fusion of the fault states of gearbox gear collected by multiple sensors, which could initially visualize the vibration states of the gear teeth in different states. Secondly, the SDP images obtained were combined with the deep learning VGG16. In this way, the local diagnostic results of each sensor can be easily obtained. Finally, the local diagnostic results of each sensor were combined with the DS evidence theory to achieve decision-level fusion, which can better realize comprehensive fault detection for gearbox gear teeth. Before fusion, the accuracies of the three sensors were 96.43%, 93.97%, and 93.28%, respectively. When sensor 1 and sensor 2 were fused, the accuracy reached 99.93%, which is 3.52% and 6.34% better than when using sensors 1 and 2, respectively, alone. When sensor 1 and sensor 3 were fused, the accuracy reached 99.96%, marking an improvement of 3.36% and 6.85% over individual use of sensors 1 and 3, respectively. When sensor 2 and sensor 3 were fused, the accuracy reached 99.40%, which is 5.78% and 6.56% better than individual use of sensors 2 and 3, respectively. When the three sensors were fused simultaneously, the accuracy reached 99.98%, which is 3.68%, 6.40%, and 7.18% better than individual use of sensors 1, 2, and 3, respectively.
引用
收藏
页数:22
相关论文
共 50 条
  • [1] Fault Diagnosis Method Based on Multi-Source Information Fusion
    Lei, Ming
    Liao, Dapeng
    Zhou, Chunsheng
    Ci, Wenbin
    Zhang, Hui
    [J]. INTERNATIONAL CONFERENCE ON ELECTRICAL AND CONTROL ENGINEERING (ICECE 2015), 2015, : 315 - 318
  • [2] Grid Fault Diagnosis Based on Information Entropy and Multi-source Information Fusion
    Zeng, Xin
    Xiong, Xingzhong
    Luo, Zhongqiang
    [J]. INTERNATIONAL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS, 2021, 67 (02) : 143 - 148
  • [3] Fault diagnosis using multi-source information fusion
    Fan, Xianfeng
    Zuo, Ming J.
    [J]. 2006 9TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION, VOLS 1-4, 2006, : 275 - 280
  • [4] Busbar fault diagnosis method based on multi-source information fusion
    Jiang, Xuebao
    Cao, Haiou
    Zhou, Chenbin
    Ren, Xuchao
    Shen, Jiaoxiao
    Yu, Jiayan
    [J]. FRONTIERS IN ENERGY RESEARCH, 2024, 12
  • [5] Rolling Bearing Fault Diagnosis Based on Multi-source Information Fusion
    Zhu, Jing
    Deng, Aidong
    Xing, Lili
    Li, Ou
    [J]. JOURNAL OF FAILURE ANALYSIS AND PREVENTION, 2024, 24 (03) : 1470 - 1482
  • [6] Fault Diagnosis of Brake Train based on Multi-Source Information Fusion
    Jin, Yongze
    Xie, Guo
    Hei, Xinhong
    Duan, Haitao
    Chen, Wenbin
    Ma, Jialin
    Zang, Qianbo
    [J]. PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 2934 - 2938
  • [7] Reciprocating Compressor Fault Diagnosis Technology Based on Multi-source Information Fusion
    Zhang M.
    Jiang Z.
    [J]. Jiang, Zhinong (jiangzhinong@263.net), 1600, Chinese Mechanical Engineering Society (53): : 46 - 52
  • [8] Fault diagnosis method for machinery based on multi-source conflict information fusion
    Wei, Jianfeng
    Zhang, Faping
    Lu, Jiping
    Yang, Xiangfei
    Yan, Yan
    [J]. MEASUREMENT SCIENCE AND TECHNOLOGY, 2022, 33 (11)
  • [9] Bearing fault diagnosis method based on multi-source heterogeneous information fusion
    Zhang, Ke
    Gao, Tianhao
    Shi, Huaitao
    [J]. MEASUREMENT SCIENCE AND TECHNOLOGY, 2022, 33 (07)
  • [10] Multi-Source Uncertain Information Fusion Method for Fault Diagnosis Based on Evidence Theory
    Mi, Jinhua
    Wang, Xinyuan
    Cheng, Yuhua
    Zhang, Songyi
    [J]. 2019 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-QINGDAO), 2019,