Variational eligibility trace meta-reinforcement recurrent network for residual life prediction of space rolling bearings
Tài liệu tham khảo
Zhang, 2022, Fault feature-extraction method of aviation bearing based on maximum correlation Rényi entropy and phase-space reconstruction technology, Entropy, 24, 1459, 10.3390/e24101459
Colas, 2021, Experimental analysis of friction and wear of self-lubricating composites used for dry lubrication of ball bearing for space applications, Lubricants, 9, 38, 10.3390/lubricants9040038
Zhang, 2016, Rolling element bearing life prediction based on multi-scale mutation particle swarm optimized multi-kernel least square support vector machine, Chin. J. Sci. Instrum., 37, 2489
Chen, 2017, Bearing life state recognition using deep sparse auto-encoder neural network with noise adding sample expansion, J. Vib. Eng., 30, 874
Dong, 2021, Life state identification method of rolling bearing based on FNER performance degradation index and IDRSN, J. Mech. Eng., 1
Gao, 2021, Reliability analysis of journal bearings inside aero gear pump based on AK-IS method, J. Beijing Univ. Aeronaut. Astronaut., 15, 1
Huang, 2021, LSTM based trajectory prediction model for cyclist utilizing multiple interactions with environment, Pattern Recognit., 112, 10.1016/j.patcog.2020.107800
Li, 2019, EA-LSTM: Evolutionary attention-based LSTM for time series prediction, Knowl.-Based Syst., 181, 104785.1, 10.1016/j.knosys.2019.05.028
Shao, 2019, Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing, Knowl.-Based Syst.
Zhang, 2020, Neural machine translation with GRU-gated attention model, IEEE Trans. Neural Netw. Learn. Syst., 31, 4688, 10.1109/TNNLS.2019.2957276
Pan, 2020, A two-stage method based on extreme learning machine for predicting the remaining useful life of rolling-element bearings, Mech. Syst. Signal Process., 144, 10.1016/j.ymssp.2020.106899
Yu, 2021, Multiscale attentional residual neural network framework for remaining useful life prediction of bearings, Measurement, 10.1016/j.measurement.2021.109310
Jakob, 2020, Hierarchical temporal memory and recurrent neural networks for time series prediction: An empirical validation and reduction to multilayer perceptrons, Neurocomputing, 396, 291, 10.1016/j.neucom.2018.09.098
Qin, 2020, Prior-knowledge and attention based meta-learning for few-shot learning, Knowl.-Based Syst., 213
Tian, 2022, Consistent meta-regularization for better meta-knowledge in few-shot learning, IEEE Trans. Neural Netw. Learn. Syst., 33, 7277, 10.1109/TNNLS.2021.3084733
Shi, 2020, A reinforced k -nearest neighbors method with application to chatter identification in high-speed milling, IEEE Trans. Ind. Electron., 67, 10844, 10.1109/TIE.2019.2962465
Yuan, 2019, A novel multi-step Q-learning method to improve data efficiency for deep reinforcement learning, Knowl.-Based Syst., 175, 107, 10.1016/j.knosys.2019.03.018
Shuai, 2020, Recruitment-imitation mechanism for evolutionary reinforcement learning, Inform. Sci., 553, 172
Patel, 2021, Grove’s algorithm in natural settings, Quantum Inf. Comput., 21, 945
Moghadam, 2022, Online optimal adaptive control of partially uncertain nonlinear discrete-time systems using multilayer neural networks, IEEE Trans. Neural Netw. Learn. Syst., 33, 4840, 10.1109/TNNLS.2021.3061414
Kou, 2020, The laser-induced damage change detection for optical elements using siamese convolutional neural networks, Appl. Soft Comput., 87, 10.1016/j.asoc.2019.106015
Li, 2020, Anomaly detection of time series with smoothness-inducing sequential variational auto-encoder, IEEE Trans. Neural Netw. Learn. Syst., 1
Sasikala, 2016, A novel memetic algorithm for discovering knowledge in binary and multi class predictions based on support vector machine, Appl. Soft Comput., 407
Ahmad, 2020, Exponentiated additive Weibull distribution, Reliab. Eng. Syst. Saf., 193, 10.1016/j.ress.2019.106663
Jia, 2020, Reliability analysis for Weibull distribution with homogeneous heavily censored data based on Bayesian and least-squares methods, Appl. Math. Model., 10.1016/j.apm.2020.02.013
Zhang, 2017, Life prediction for rolling bearings utilizing both failure and truncated samples, J. Vib. Shock, 36, 10