Citation: | XIE Fengyun, DONG Jiankun, FU Yu, LIU Yi, XIAO Qian. Motor Fault Diagnosis Method Based on Migration Learning and CNN[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(3): 513-519. doi: 10.13433/j.cnki.1003-8728.20220196 |
[1] |
符羽. 基于卷积神经网络的三相异步电机故障诊断方法研究[D]. 南昌: 华东交通大学, 2021.
FU Y. Fault diagnosis of three phase induction motor based on convolution neural network[D]. Nanchang: East China Jiaotong University, 2021. (in Chinese)
|
[2] |
郝鹏永. 基于深度信念网络的异步电机典型故障的诊断方法及其实验研究[D]. 秦皇岛: 燕山大学, 2019.
HAO P Y. Diagnostic method and experimental study of typical faults of asynchronous motor based on deep belief network[D]. Qinghuangdao: Yanshan University, 2019. (in Chinese)
|
[3] |
贺珂珂. 基于深度学习理论的电机故障诊断方法研究[D]. 兰州: 兰州理工大学, 2019.
HE K K. Research on motor fault diagnosis method based on deep learning theory[D]. Lanzhou: Lanzhou University of Technology, 2019. (in Chinese)
|
[4] |
HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
|
[5] |
仝钰, 庞新宇, 魏子涵. 基于GADF-CNN的滚动轴承故障诊断方法[J]. 振动与冲击, 2021, 40(5): 247-253.
TONG Y, PANG X Y, WEI Z H. Fault diagnosis method of rolling bearing based on GADF-CNN[J]. Journal of Vibration and Shock, 2021, 40(5): 247-253. (in Chinese)
|
[6] |
丁承君, 冯玉伯, 王曼娜. 基于变分模态分解与深度卷积神经网络的滚动轴承故障诊断[J]. 振动与冲击, 2021, 40(2): 287-296.
DING C J, FENG Y B, WANG M N. Rolling bearing fault diagnosis using variational mode decomposition and deep convolutional neural network[J]. Journal of Vibration and Shock, 2021, 40(2): 287-296. (in Chinese)
|
[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 Y G, JIA F, ZHOU X, et al. A deep learning-based method for machinery health monitoring with big data[J]. Journal of Mechanical Engineering, 2015, 51(21): 49-56. (in Chinese) doi: 10.3901/JME.2015.21.049
|
[8] |
孙文珺, 邵思羽, 严如强. 基于稀疏自动编码深度神经网络的感应电动机故障诊断[J]. 机械工程学报, 2016, 52(9): 65-71. doi: 10.3901/JME.2016.09.065
SUN W J, SHAO S Y, YAN R Q. Induction motor fault diagnosis based on deep neural network of sparse auto-encoder[J]. Journal of Mechanical Engineering, 2016, 52(9): 65-71. (in Chinese) doi: 10.3901/JME.2016.09.065
|
[9] |
王丽华, 谢阳阳, 张永宏, 等. 采用深度学习的异步电机故障诊断方法[J]. 西安交通大学学报, 2017, 51(10): 128-134. doi: 10.7652/xjtuxb201710021
WANG L H, XIE Y Y, ZHANG Y H, et al. A fault diagnosis method for asynchronous motor using deep learning[J]. Journal of Xi′an Jiaotong University, 2017, 51(10): 128-134. (in Chinese) doi: 10.7652/xjtuxb201710021
|
[10] |
陈祝云. 基于深度迁移学习的机械设备智能诊断方法研究[D]. 广州: 华南理工大学, 2020.
CHEN Z Y. Research on intelligent diagnosis of machinery equipment based on deep transfer learning[D]. Guangzhou: South China University of Technology, 2020. (in Chinese)
|
[11] |
陈超, 沈飞, 严如强. 改进LSSVM迁移学习方法的轴承故障诊断[J]. 仪器仪表学报, 2017, 38(1): 33-40. doi: 10.3969/j.issn.0254-3087.2017.01.005
CHEN C, SHEN F, YAN R Q. Enhanced least squares support vector machine-based transfer learning strategy for bearing fault diagnosis[J]. Chinese Journal of Scientific Instrument, 2017, 38(1): 33-40. (in Chinese) doi: 10.3969/j.issn.0254-3087.2017.01.005
|
[12] |
康守强, 胡明武, 王玉静, 等. 基于特征迁移学习的变工况下滚动轴承故障诊断方法[J]. 中国电机工程学报, 2019, 39(3): 764-772. doi: 10.13334/J.0258-8013.PCSEE.180130
KANG S Q, HU M W, WANG Y J, et al. Fault diagnosis method of a rolling bearing under variable working conditions based on feature transfer learning[J]. Proceedings of the CSEE, 2019, 39(3): 764-772. (in Chinese) doi: 10.13334/J.0258-8013.PCSEE.180130
|
[13] |
沈飞, 陈超, 严如强. 奇异值分解与迁移学习在电机故障诊断中的应用[J]. 振动工程学报, 2017, 30(1): 118-126. doi: 10.16385/j.cnki.issn.1004-4523.2017.01.016
SHEN F, CHEN C, YAN R Q. Application of SVD and transfer learning strategy on motorfault diagnosis[J]. Journal of Vibration Engineering, 2017, 30(1): 118-126. (in Chinese) doi: 10.16385/j.cnki.issn.1004-4523.2017.01.016
|
[14] |
朱光磊. 三相笼型异步电机故障时的定子电流特征分析与实验研究[D]. 广州: 华南理工大学, 2018.
ZHU G L. Study of the current characteristics in three-phase squirrel cage asynchronous motor fault state and experimental verification[D]. Guangzhou: South China University of Technology, 2018. (in Chinese)
|
[15] |
高学, 王有旺. 基于CNN和随机弹性形变的相似手写汉字识别[J]. 华南理工大学学报(自然科学版), 2014, 42(1): 72-76.
GAO X, WANG Y W. Recognition of similar handwritten Chinese characters based on CNN and random elastic deformation[J]. Journal of South China University of Technology (Natural Science Edition), 2014, 42(1): 72-76. (in Chinese)
|
[16] |
于洋, 何明, 刘博, 等. 基于TL-LSTM的轴承故障声发射信号识别研究[J]. 仪器仪表学报, 2019, 40(5): 51-59.
YU Y, HE M, LIU B, et al. Research on acoustic emission signal recognition of bearing fault based on TL-LSTM[J]. Chinese Journal of Scientific Instrument, 2019, 40(5): 51-59. (in Chinese)
|
[17] |
YANG Y H, SHI G Q, SHI X. Fault monitoring and classification of rotating machine based on PCA and KNN[C]//2018 Chinese Control and Decision Conference. Shenyang: IEEE, 2018: 1795-1800.
|