| Citation: |
CUI Wei，MENG Guoying，WAN Xingwei. Fault diagnosis method of rolling bearing of mine main fan based on transfer learning[J]. Coal Science and Technology，2024，52（S1）：280−287. DOI: 10.12438/cst.2023-0903
|
The condition monitoring and fault diagnosis of the rolling bearings of the main fan in the mine are significant to the safety of coal mine production. The existing fault diagnosis methods of rolling bearing have the problems of insufficient training and accuracy when applied directly in actual working conditions. Moreover, the rolling bearings of the mine main fan are in normal operation for a long time, and the number of normal samples is much more than the faulty samples, so there is a sample imbalance problem. Therefore, this paper proposes a fault diagnosis method for rolling bearings of mine main fan based on transfer learning. The method takes the conventional rolling bearing data as the source domain data and the mine main fan rolling bearing data as the target domain data. Firstly, the one-dimensional vibration signal is converted into two-dimensional SDP images using the SDP method, and then the conventional rolling bearing fault diagnosis model is trained using sufficient source domain image samples. After training, the parameters of the diagnostic model are transferred to the mine main fan rolling bearing fault diagnosis model, and the lower layer network is locked and the higher layer network of the model is fine-tuned by the target domain image samples during the transfer process, and finally the mine main fan rolling bearing fault diagnosis model with optimized parameter weights is obtained. Meanwhile, in order to solve the sample imbalance problem, a weighted cross-entropy loss function is added to the model for training, so that the diagnosis model gives higher weights to the fault samples as a minority class and pays more attention to the fault samples in the diagnosis process, thus improving the diagnosis accuracy. In order to verify the effectiveness of the proposed method, this paper uses a conventional rolling bearing fault test bench and the rolling bearing data of the mine main fan fan in actual working conditions for experimental verification. The results show that the proposed method can accurately identify and classify the operating status of the mine main fan rolling bearings, and the accuracy rate is 99.28%.
| [1] |
刘洪文,李志常. 大型矿用风机配套电机轴承结构型式的研究[J]. 防爆电机,2010,45(2):7−9. doi: 10.3969/j.issn.1008-7281.2010.02.003
LIU Hongwen,LI Zhichang. Research on structure type of motor bearing for large mine fan[J]. Explosion-proof Electric Machine,2010,45(2):7−9. doi: 10.3969/j.issn.1008-7281.2010.02.003
|
| [2] |
ZHAO R,YAN R,CHEN Z,et al. Deep learning and its applications to machine health monitoring[J]. Mechanical Systems and Signal Processing,2019,115:213−237. doi: 10.1016/j.ymssp.2018.05.050
|
| [3] |
LEI Y,YANG B,JIANG X,et al. Applications of machine learning to machine fault diagnosis:a review and roadmap[J]. Mechanical Systems and Signal Processing,2020,138:106587. doi: 10.1016/j.ymssp.2019.106587
|
| [4] |
LIU R,YANG B,ZIO E,et al. Artificial intelligence for fault diagnosis of rotating machinery:a review[J]. Mechanical Systems and Signal Processing,2018,108:33−47. doi: 10.1016/j.ymssp.2018.02.016
|
| [5] |
WANG Y,HUANG S,DAI J,et al. A novel bearing fault diagnosis methodology based on SVD and one-dimensional convolutional neural network[J]. Shock and Vibration,2020:1−17. doi: 10.1155/2020/1850286.
|
| [6] |
SHAO H,JIANG H,ZHANG H,et al. Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing[J]. Mechanical Systems and Signal Processing,2018,100:743−765. doi: 10.1016/j.ymssp.2017.08.002
|
| [7] |
侯文擎,叶鸣,李巍华. 基于改进堆叠降噪自编码的滚动轴承故障分类[J]. 机械工程学报,2018,54(7):87−96. doi: 10.3901/JME.2018.07.087
HOU Wenqing,YE Ming,LI Weihua. Fault classification of rolling bearings based on improved stack noise reduction self-coding[J]. Chinese Journal of Mechanical Engineering,2018,54(7):87−96. doi: 10.3901/JME.2018.07.087
|
| [8] |
SUN Y,LI S. Bearing fault diagnosis based on optimal convolution neural network[J]. Measurement,2022,190:110702. doi: 10.1016/j.measurement.2022.110702
|
| [9] |
LI X,ZHANG W,DING Q. Cross-domain fault diagnosis of rolling element bearings using deep generative neural networks[J]. IEEE Transactions on Industrial Electronics,2018,66(7):5525−5534.
|
| [10] |
ZHANG W,LI C,PENG G,et al. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load[J]. Mechanical Systems and Signal Processing,2018,100:439−453. doi: 10.1016/j.ymssp.2017.06.022
|
| [11] |
张旭辉,潘格格,郭欢欢,等. 基于深度迁移学习的采煤机摇臂部滚动轴承故障诊断方法[J]. 煤炭科学技术,2022,50(4):256−263
ZHANG Xuhui,PAN Gege,GUO Huanhuan,et al. Fault diagnosis method of roller bearing of shearer rocker arm based on deep transfer learning[J]. Coal Science and Technology,2022,50(4):256−263.
|
| [12] |
TONG Z,LI W,ZHANG B,et al. Bearing fault diagnosis under variable working conditions based on domain adaptation using feature transfer learning[J]. IEEE Access,2018,6:76187−76197. doi: 10.1109/ACCESS.2018.2883078
|
| [13] |
TONG Z,LI W,ZHANG B,et al. Bearing fault diagnosis based on domain adaptation using transferable features under different working conditions[J]. Shock and Vibration,2018:1−12.
|
| [14] |
DU Y,WANG A,WANG S,et al. Fault diagnosis under variable working conditions based on STFT and transfer deep residual network[J]. Shock and Vibration,2020:1−18.
|
| [15] |
雷亚国,杨彬,杜兆钧,等. 大数据下机械装备故障的深度迁移诊断方法[J]. 机械工程学报,2019,55(7):1−8. doi: 10.3901/JME.2019.07.001
LEI Yaguo,YANG Bin,DU Zhaojun,et al. Deep migration diagnosis method for mechanical equipment faults based on big data[J]. Chinese Journal of Mechanical Engineering,2019,55(7):1−8. doi: 10.3901/JME.2019.07.001
|
| [16] |
邵海东,张笑阳,程军圣,等. 基于提升深度迁移自动编码器的轴承智能故障诊断[J]. 机械工程学报,2020,56(9):84−90.
SHAO Haidong,ZHANG Xiaoyang,CHENG Junsheng,et al. Bearing intelligent fault diagnosis based on uplifting deep migration autoencoder [J]. Chinese Journal of Mechanical Engineering,2019,56(9):84−90.
|
| [17] |
尹晓伟,江雪峰,王龙福. 风电机组轴承故障诊断与疲劳寿命研究综述[J]. 轴承,2022(5):1−8.
YIN Xiaowei,JIANG Xuefeng,WANG Longfu. Review on fault diagnosis and fatigue life of wind turbine bearing[J]. Bearing,2022(5):1−8.
|
| [18] |
张运生,乔建伟,赵岩,等. YBF系列风机电机的滚动轴承[J]. 电气防爆,2011(2):12−15. doi: 10.3969/j.issn.1004-9118.2011.02.004
ZHANG Yunsheng,QIAO Jianwei,ZHAO Yan,et al. Rolling bearing of YBF series fan motor[J]. Electric Explosion-Proof,2011(2):12−15. doi: 10.3969/j.issn.1004-9118.2011.02.004
|
| [19] |
孟宗,关阳,潘作舟,等. 基于二次数据增强和深度卷积的滚动轴承故障诊断研究[J]. 机械工程学报,2021,57(23):106−115. doi: 10.3901/JME.2021.23.106
MENG Zong,GUAN Yang,PAN Zuozhou,et al. Research on fault diagnosis of rolling bearing based on secondary data enhancement and deep convolution[J]. Chinese Journal of Mechanical Engineering,2021,57(23):106−115. doi: 10.3901/JME.2021.23.106
|
| [20] |
ZHANG X,MENG G,WANG A,et al. Study on fault diagnosis method for the tail rope of a hoisting system based on machine vision[J]. Advances in Mechanical Engineering,2022,14(8):1−13.
|
| [21] |
XU X,LIU H,ZHU H,et al. Fan fault diagnosis based on symmetrized dot pattern analysis and image matching[J]. Journal of Sound and Vibration,2016,374:297−311. doi: 10.1016/j.jsv.2016.03.030
|
| [22] |
SUN Y,LI S,WANG Y,et al. Fault diagnosis of rolling bearing based on empirical mode decomposition and improved manhattan distance in symmetrized dot pattern image[J]. Mechanical Systems and Signal Processing,2021,159:107817. doi: 10.1016/j.ymssp.2021.107817
|
| [23] |
邵思羽. 基于深度学习的旋转机械故障诊断方法研究[D]. 南京:东南大学,2019.
SHAO Siyu. Research on rotating machinery fault diagnosis method based on deep learning [D]. Nanjing:Southeast University,2019.
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: