Citation: | WU Dongmei, WANG Fuqi, LI Xiangong, et al. Bearing intelligent fault diagnosis[J]. Journal of Mine Automation,2022,48(9):49-55. doi: 10.13272/j.issn.1671-251x.17986 |
[1] |
宫涛,杨建华,单振,等. 强噪声背景与变转速工况条件下滚动轴承故障诊断研究[J]. 工矿自动化,2021,47(7):63-71.
GONG Tao,YANG Jianhua,SHAN Zhen,et al. Research on rolling bearing fault diagnosis under strong noise background and variable speed working condition[J]. Industry and Mine Automation,2021,47(7):63-71.
|
[2] |
黄重谦. 基于多隐层小波卷积极限学习神经网络的滚动轴承故障识别[J]. 工矿自动化,2021,47(7):63-71.
HUANG Zhongqian. Fault identification of rolling bearing based on multi hidden layers wavelet convolution extreme learning neural network[J]. Industry and Mine Automation,2021,47(7):63-71.
|
[3] |
吴静然,丁恩杰,崔冉,等. 采用多尺度注意力机制的旋转机械故障诊断方法[J]. 西安交通大学学报,2020,54(2):51-58.
WU Jingran,DING Enjie,CUI Ran,et al. A diagnostic approach for rotating machinery using multi-scale feature attention mechanism[J]. Journal of Xi'an Jiaotong University,2020,54(2):51-58.
|
[4] |
HE Miao,HE D. Deep learning based approach for bearing fault diagnosis[J]. IEEE Transactions on Industry Applications,2017,53(3):3057-3065. doi: 10.1109/TIA.2017.2661250
|
[5] |
LI Xiangong,ZHANG Yuzhi,WANG Fuqi,et al. A fault diagnosis method of rolling bearing based on wavelet packet analysis and deep forest[J]. Symmetry,2022,14(2):267. doi: 10.3390/sym14020267
|
[6] |
汪峰,周凤星,严保康. 基于特征量融合和支持向量机的滚动轴承故障诊断[J]. 科学技术与工程,2022,22(6):2351-2356. doi: 10.3969/j.issn.1671-1815.2022.06.026
WANG Feng,ZHOU Fengxing,YAN Baokang. Rolling bearing fault diagnosis based on feature fusion and support vector machine[J]. Science Technology and Engineering,2022,22(6):2351-2356. doi: 10.3969/j.issn.1671-1815.2022.06.026
|
[7] |
雷亚国,贾峰,周昕,等. 基于深度学习理论的机械装备大数据健康监测方法[J]. 机械工程学报,2015,51(21):49-56. doi: 10.3901/JME.2015.21.049
LEI Yaguo,JIA Feng,ZHOU Xi,et al. A deep learning based method for machinery health monitoring with big data[J]. Journal of Mechanical Engineering,2015,51(21):49-56. doi: 10.3901/JME.2015.21.049
|
[8] |
WANG Lihua,ZHAO Xiaoping,WU Jiaxin,et al. Motor fault diagnosis based on short-time fourier transform and convolutional neural network[J]. Chinese Journal of Mechanical Engineering,2017,30(6):1357-1368. doi: 10.1007/s10033-017-0190-5
|
[9] |
LEVENT E,TURKER I,SERKAN K. A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier[J]. Journal of Signal Processing Systems for Signal,Image,and Videl Technology,2019,91(2):179-189. doi: 10.1007/s11265-018-1378-3
|
[10] |
李长文,程泽银,张小刚,等. 基于深度残差网络的采煤机摇臂齿轮故障诊断[J]. 工矿自动化,2021,47(3):71-78.
LI Changwen,CHENG Zeyin,ZHANG Xiaogang,et al. Fault diagnosis of shearer rocker gear based on deep residual network[J]. Industry and Mine Automation,2021,47(3):71-78.
|
[11] |
姜家国,郭曼利,杨思国. 基于GAF和DenseNet的滚动轴承故障诊断方法[J]. 工矿自动化,2021,47(8):84-89.
JIANG Jiaguo,GUO Manli,YANG Siguo. Fault diagnosis of rolling barings based on GAF and DenseNet[J]. Industry and Mine Automation,2021,47(8):84-89.
|
[12] |
LEI Jinhao,LIU Chao,JIANG Dongxiang. Fault diagnosis of wind turbine based on long short-term memory networks[J]. Renewable Energy,2019,133:422-432. doi: 10.1016/j.renene.2018.10.031
|
[13] |
樊家伟,郭瑜,伍星,等. 基于LSTM神经网络和故障特征增强的行星齿轮箱故障诊断[J]. 振动与冲击,2021,40(20):271-277. doi: 10.13465/j.cnki.jvs.2021.20.034
FAN Jiawei,GUO Yu,WU Xing,et al. Fault diagnosis of planetary gearboxes based on LSTM neural network and fault feature enhancement[J]. Journal of Vibration and Shock,2021,40(20):271-277. doi: 10.13465/j.cnki.jvs.2021.20.034
|
[14] |
张立鹏,毕凤荣,程建刚,等. 基于注意力Bi GRU的机械故障诊断方法研究[J]. 振动与冲击,2021,40(5):113-118.
ZHANG Lipeng,BI Fengrong,CHENG Jiangang,et al. Mechanical fault diagnosis method based on attention Bi GRU[J]. Journal of Vibration and Shock,2021,40(5):113-118.
|
[15] |
吉兴全,曾若海,张玉敏,等. 基于注意力机制的CNN-LSTM短期电价预测[J]. 电力系统保护玉控制,2022,50(17):125-132.
JI Xingquan,ZENG Ruohai,ZHANG Yumin,et al. CNN-LSTM short-term electricity prediction based on an attention mechanism[J]. Power System Protection and Control,2022,50(17):125-132.
|
[16] |
毛煜,尚海昆,于卓琦. 基于长短期记忆网络的电网同调机群快速辨识[J]. 电气工程学报,2022,17(2):201-207.
MAO Yu,SHANG Haikun,YU Zhuoqi. A fast prediction method of coherernt generators based on long short-term memory network[J]. Journal of Electrical Engineering,2022,17(2):201-207.
|
[17] |
MAATEN L V,HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research,2008,9(11):2579-2605.
|