1 |
沈保明, 陈保家, 赵春华, 等. 深度学习在机械设备故障预测与健康管理中的研究综述[J]. 机床与液压, 2021, 49(19): 162-171.
|
|
SHEN B M, CHEN B J, ZHAO C H, et al. Review on the research of deep learning in mechanical equipment fault prognostics and health management[J]. Machine Tool & Hydraulics, 2021, 49(19): 162-171 (in Chinese).
|
2 |
许艳雷, 邱明, 李军星, 等. 基于SKF-KF-Bayes的滚动轴承剩余使用寿命预测方法[J]. 振动与冲击, 2021, 40(19): 26-31, 40.
|
|
XU Y L, QIU M, LI J X, et al. Remaining useful life prediction method of rolling bearing based on SKF-KF-Bayes[J]. Journal of Vibration and Shock, 2021, 40(19): 26-31, 40 (in Chinese).
|
3 |
DAI Y, CHENG S, GAN Q J, et al. Life prediction of Ni-Cd battery based on linear Wiener process[J]. Journal of Central South University, 2021, 28(9): 2919-2930.
|
4 |
WANG Y D, ZHAO Y F, ADDEPALLI S. Remaining useful life prediction using deep learning approaches: A review[J]. Procedia Manufacturing, 2020, 49: 81-88.
|
5 |
SATEESH BABU G, ZHAO P L, LI X L. Deep convolutional neural network based regression approach for estimation of remaining useful life[J]. International Conference on Database Systems for Advanced Applications, 2016, 9642: 214-228.
|
6 |
LI X, DING Q, SUN J Q. Remaining useful life estimation in prognostics using deep convolution neural networks[J]. Reliability Engineering & System Safety, 2018, 172: 1-11.
|
7 |
SAXENA A, GOEBEL K, SIMON D, et al. Damage propagation modeling for aircraft engine run-to-failure simulation[C]∥ 2008 International Conference on Prognostics and Health Management. Piscataway: IEEE Press, 2008: 10423504.
|
8 |
SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]∥ 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2015: 1-9.
|
9 |
LI H. Remaining useful life prediction using multi-scale deep convolutional neural network[J]. Applied Soft Computing, 2020, 89: 106113.
|
10 |
LIM P, GOH C K, TAN K C, et al. Estimation of remaining useful life based on switching Kalman filter neural network ensemble[J]. Annual Conference of the Prognostics and Health Management Society, 2014, 6(1): 1-9.
|
11 |
ZHENG S, RISTOVSKI K, FARAHAT A, et al. Long short-term memory network for remaining useful life estimation[C]∥ 2017 IEEE International Conference on Prognostics and Health Management. Piscataway: IEEE Press, 2017: 88-95.
|
12 |
GUO L, LI N, JIA F, et al. A recurrent neural network based health indicator for remaining useful life prediction of bearings[J]. Neurocomputing, 2017, 240: 98-109.
|
13 |
HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
|
14 |
CHO K, VAN MERRIENBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[DB/OL]. arXiv preprint: 1406.1078, 2014.
|
15 |
CHEN J L, JING HJ, CHANG Y H, et al. Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process[J]. Reliability Engineering & System Safety, 2019, 185: 372-382.
|
16 |
MA M, MAO Z. Deep-convolution-based LSTM network for remaining useful life prediction[J]. IEEE Transactions on Industrial Informatics, 2021, 17(3): 1658-1667.
|
17 |
刘畅, 陈雯柏. 一种基于MSDCNN-LSTM的设备RUL预测方法[J]. 西北工业大学学报, 2021, 39(2): 407-413.
|
|
LIU C, CHEN W B. A RUL prediction method of equipments based on MSDCNN-LSTM[J]. Journal of Northwestern Polytechnical University, 2021, 39(2): 407-413 (in Chinese).
|
18 |
KHAN S, YAIRI T. A review on the application of deep learning in system health management[J]. Mechanical Systems and Signal Processing, 2018, 107: 241-265.
|
19 |
PILLAI S, VADAKKEPAT P. Two stage deep learning for prognostics using multi-loss encoder and convolutional composite features[J]. Expert Systems with Applications, 2021, 171: 114569.
|
20 |
HEIMES F O. Recurrent neural networks for remaining useful life estimation[C]∥ 2008 International Conference on Prognostics and Health Management. Piscataway: IEEE Press, 2008: 10423512.
|
21 |
ZHU H G, ZENG H, LIU J, et al. Logish: A new nonlinear nonmonotonic activation function for convolutional neural network[J]. Neurocomputing, 2021, 458: 490-499.
|
22 |
WANG Q L, WU B G, ZHU P F, et al. ECA-net: Efficient channel attention for deep convolutional neural networks[C]∥ 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2020: 11531-11539.
|
23 |
KINGMA D P, BA J L. Adam: A method for stochastic optimization[DB/OL]. arXiv preprint: 1412.6980, 2014.
|
24 |
WANG B, LEI Y, LI N, et al. Deep separable convolutional network for remaining useful life prediction of machinery[J]. Mechanical Systems and Signal Processing, 2019, 134: 106330.
|