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

被引:9
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 31 条
[1]   A real-time fault diagnosis method for hypersonic air vehicle with sensor fault based on the auto temporal convolutional network [J].
Ai, Shaojie ;
Song, Jia ;
Cai, Guobiao .
AEROSPACE SCIENCE AND TECHNOLOGY, 2021, 119
[2]   Fault diagnosis of a centrifugal pump using MLP-GABP and SVM with CWT [J].
ALTobi, Maamar Ali Saud ;
Bevan, Geraint ;
Wallace, Peter ;
Harrison, David ;
Ramachandran, K. P. .
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH, 2019, 22 (03) :854-861
[3]   Increase wind gearbox power density by means of IGS (Improved Gear Surface) [J].
Carranza Fernandez, Ruben ;
Tobie, Thomas ;
Collazo, Joaquin .
INTERNATIONAL JOURNAL OF FATIGUE, 2022, 159
[4]  
Case Western Reserve University, CAS W RES U
[5]   Multi-Sensor GA-BP Algorithm Based Gearbox Fault Diagnosis [J].
Fu, Yuan ;
Liu, Yu ;
Yang, Yan .
APPLIED SCIENCES-BASEL, 2022, 12 (06)
[6]   Bearing fault diagnosis with varying conditions using angular domain resampling technology, SDP and DCNN [J].
Gu, Yingkui ;
Zeng, Lei ;
Qiu, Guangqi .
MEASUREMENT, 2020, 156
[7]   Fault diagnosis of multi-channel data by the CNN with the multilinear principal component analysis [J].
Guo, Yiming ;
Zhou, Yifan ;
Zhang, Zhisheng .
MEASUREMENT, 2021, 171
[8]   A deep multi-signal fusion adversarial model based transfer learning and residual network for axial piston pump fault diagnosis [J].
He, You ;
Tang, Hesheng ;
Ren, Yan ;
Kumar, Anil .
MEASUREMENT, 2022, 192
[9]   Real-time SVD-based detection of multiple combined faults in induction motors [J].
Hernandez-Vargas, M. ;
Cabal-Yepez, E. ;
Garcia-Perez, A. .
COMPUTERS & ELECTRICAL ENGINEERING, 2014, 40 (07) :2193-2203
[10]   Track circuit fault prediction method based on grey theory and expert system [J].
Hu, Li-Qiang ;
He, Chao-Feng ;
Cai, Zhao-Quan ;
Wen, Long ;
Ren, Teng .
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2019, 58 :37-45