1 |
LUO P, HU N Q, ZHANG L, et al. Adaptive fisher-based deep convolutional neural network and its application to recognition of rolling element bearing fault patterns and sizes[J]. Mathematical Problems in Engineering, 2020, 2020: No.3409262. 10.1155/2020/3409262
|
2 |
ZHANG Z Q, ZHOU F N, CHEN D M. Application of improved parallel LSTM in bearing fault diagnosis[C]// Proceedings of the 2019 Chinese Automation Congress. Piscataway: IEEE, 2019: 5755-5760. 10.1109/cac48633.2019.8997417
|
3 |
WANG X, MAO D X, LI X D. Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network[J]. Measurement, 2021, 173: No.108518. 10.1016/j.measurement.2020.108518
|
4 |
李益兵,王磊,江丽. 基于PSO改进深度置信网络的滚动轴承故障诊断[J]. 振动与冲击, 2020, 39(5): 89-96.
|
|
LI Y B, WANG L, JIANG L. Rolling bearing fault diagnosis based on DBN algorithm improved with PSO[J]. Journal of Vibration and Shock, 2020, 39(5): 89-96.
|
5 |
WANG Z, LIU Q X, CHEN H S, et al. A deformable CNN-DLSTM based transfer learning method for fault diagnosis of rolling bearing under multiple working conditions[J]. International Journal of Production Research, 2020, 59(16): 4811-4825. 10.1080/00207543.2020.1808261
|
6 |
WU J Y, ZHAO Z B, SUN C, et al. Few-shot transfer learning for intelligent fault diagnosis of machine[J]. Measurement, 2020, 166: No.108202. 10.1016/j.measurement.2020.108202
|
7 |
CHEN Y H, PENG G L, XIE C H, et al. ACDIN: bridging the gap between artificial and real bearing damages for bearing fault diagnosis[J]. Neurocomputing, 2018, 294: 61-71. 10.1016/j.neucom.2018.03.014
|
8 |
陈仁祥,陈思杨,杨黎霞,等. 改进TrAdaBoost多分类算法的滚动轴承故障诊断[J]. 振动与冲击, 2019, 38(15): 36-41, 48. 10.13465/j.cnki.jvs.2019.15.005
|
|
CHEN R X, CHEN S Y, YANG L X, et al. Fault diagnosis of rolling bearings based on improved TrAdaBoost multi-classification algorithm[J]. Journal of Vibration and Shock, 2019, 38(15): 36-41, 48. 10.13465/j.cnki.jvs.2019.15.005
|
9 |
LI X D, HU Y, LI M T, et al. Fault diagnostics between different type of components: a transfer learning approach[J]. Applied Soft Computing, 2020, 86: No.105950. 10.1016/j.asoc.2019.105950
|
10 |
KANG S Q, QIAO C Y, WANG Y J, et al. Fault diagnosis method of rolling bearings under varying working conditions based on deep feature transfer[J]. Journal of Mechanical Science and Technology, 2020, 34(11): 4383-4391. 10.1007/s12206-020-1003-9
|
11 |
BRUNA J, ZAREMBA W, SZLAM A, et al. Spectral networks and locally connected networks on graphs[EB/OL]. (2014-05-21) [2020-06-01]..
|
12 |
ZHANG Z W, CUI P, ZHU W W. Deep learning on graphs: a survey[J]. IEEE Transactions on Knowledge and Data Engineering, 2020(Early Access). 10.1109/tkde.2020.2981333
|
13 |
HAMMOND D K, VANDERGHEYNST P, GRIBONVAL R. Wavelets on graphs via spectral graph theory[J]. Applied and Computational Harmonic Analysis, 2011, 30(2): 129-150. 10.1016/j.acha.2010.04.005
|
14 |
KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[EB/OL]. (2017-02-22) [2020-06-01]..
|
15 |
SHEN J, QU Y R, ZHANG W N, et al. Wasserstein distance guided representation learning for domain adaptation[C]// Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2018: 4058-4065
|
16 |
Case Western Reserve University. Bearing Data Center[EB/OL]. [2020-06-01].. 10.17925/ohr.2016.12.01.38
|
17 |
QIU H, LEE J, LIN J, et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J]. Journal of Sound and Vibration, 2006, 289(4/5): 1066-1090. 10.1016/j.jsv.2005.03.007
|
18 |
Prognostics and Health Management Society. Data analysis competition[EB/OL]. [2020-06-01].. 10.1109/phm-besanon49106.2020
|
19 |
LESSMEIER C, KIMOTHO J K, ZIMMER D, et al. Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: a benchmark data set for data-driven classification[C]// Proceedings of the 3rd European Conference of the Prognostics and Health Management Society. State College, PA: PHM Society, 2016:1-17.
|
20 |
PAN S J, TSANG I W, KWOK J T, et al. Domain adaptation via transfer component analysis[J]. IEEE Transactions on Neural Networks, 2011, 22(2): 199-210. 10.1109/tnn.2010.2091281
|
21 |
LU W N, LIANG B, CHENG Y, et al. Deep model based domain adaptation for fault diagnosis[J]. IEEE Transactions on Industrial Electronics, 2017, 64(3): 2296-2305. 10.1109/tie.2016.2627020
|
22 |
WANG Q, MICHAU G, FINK O. Domain adaptive transfer learning for fault diagnosis[C]// Proceedings of the 2019 Prognostics and System Health Management Conference. Piscataway: IEEE, 2019: 279-285. 10.1109/phm-paris.2019.00054
|
23 |
GUO L, LEI Y G, XING S B, et al. Deep convolutional transfer learning network: a new method for intelligent fault diagnosis of machines with unlabeled data[J]. IEEE Transactions on Industrial Electronics, 2019, 66(9): 7316-7325. 10.1109/tie.2018.2877090
|
24 |
YANG B, LEI Y G, JIA F, et al. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings[J]. Mechanical Systems and Signal Processing, 2019, 122: 692-706. 10.1016/j.ymssp.2018.12.051
|