[2] SI X S,HU C H,ZHANG Z X.Data-driven remaining useful life prognosis techniques[M].New York: Springer, 2017.
[4] LIAO L,KOTTIG F.Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems,and an application to battery life prediction[J].IEEE Transactions on Reliability,2014,63(1): 191-207.
[5] JARDINE A,LIN D,BANJEVIC D.A review on machinery diagnostics and prognostics implementing condition-based maintenance[J].Mecha-nical Systems and Signal Processing, 2006,20(7): 1483-1510.
[9] MOGHADDASS R,ZUO M J.An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process[J].Reliability Engineering and System Safety,2014,124: 92-104.
[10] PECHT M G.Prognostics and health management of electronics[M].Hoboken: Wiley,2008.
[11] LI C J,LEE H.Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics[J].Mechanical Systems and Signal Processing,2005,19(4): 836-846.
[12] SI X S,WANG W B,HU C H,et al.Remaining useful life estimation-a review on the statistical data driven approaches[J].European Journal of Operational Research, 2011,213(1): 1-14.
[13] KHAN S,TAKEHISA Y.A review on the application of deep learning in system health management[J].Mechanical Systems and Signal Processing, 2018,107: 241-265.
[15] WANG Y,ZHAO Y,ADDEPALLI S.Remaining useful life prediction using deep learning approaches: a review[J].Procedia Manufacturing,2020,49: 81-88.
[16] DEUTSCH J,HE D.Using deep learning-based approach to predict remaining useful life of rotating components[J].IEEE Transactions on Systems,Man,and Cybernetics: Systems,2017,48(1): 11-20.
[21] ZHANG S,ZHANG C,YOU Z,et al.Asynchronous stochastic gradient descent for DNN training[C]//International Conference on Acoustics,Speech and Signal Processing, IEEE,2013: 6660-6663.
[22] ZHOU X,HSIEH S J,PENG B,et al.Cycle life estimation of lithium-ion polymer batteries using artificial neural network and support vector machine with time resolved thermography[J].Microelectronics Reliability,2017,79: 48-58.
[23] ELFORJANI M,SHANBR S.Prognosis of bearing acoustic emission signals using supervised machine learning[J].IEEE Transactions on Industrial Electronics,2018,65(7): 5864-5871.
[24] KHAN F,EKER O F,JENNIONS I K,et al.Prognostics of crack propagation in structures using time delay neural network[C]//IEEE Conference on Prognostics and Health Ma-nagement(PHM),2015: 1-6.
[25] LI X,ELASHA F,SHANBR S,et al.Remaining useful life prediction of rolling element bearings using supervised machine learning[J].Energies,2019,12(14): 2705.
[26] BASTAMI A R,AASI A,ARGHAND H A.Estimation of remaining useful life of rolling element bearings using wavelet packet decomposition and artificial neural network[J].Iranian Journal of Science and Technology,Transactions of Electrical Engineering,2019,43(1): 233-245.
[27] SUN C,MA M,ZHAO Z,et al.Deep transfer learning based on sparse autoencoder for remaining useful life prediction of tool in manufac-turing[J].IEEE Transactions on Industrial Informatics,2018,15(4): 2416-2425.
[29] PENG K,JIAO R,DONG J,et al.A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter[J].Neurocomputing,2019,361: 19-28.
[30] ZHAO G,LIU X,ZHANG B,et al.Bearing health condition prediction using deep belief network[C]//Proceedings of the Annual Conference of Prognostics and Health Ma-nagement Society,2017: 1-8.
[31] SLOUKIA F,EL AROUSSI M,ME-DROMI H,et al.Bearings prognostic using mixture of Gaussians hidden Markov model and support vector machine[C]//ACS International Conference on Computer Systems and Applications(AICCSA), IEEE, 2013: 1-4.
[32] SHAO H,JIANG H,LI X,et al.Rolling bearing fault detection us-ing continuous deep belief network with locally linear embedding[J].Computers in Industry,2018,96: 27-39.
[34] ZHAO G Q,ZHANG G H,LIU Y F, et al.Lithium-ion battery remaining useful life prediction with deep belief network and relevance vector machine[C]//IEEE International Conference on Prognostics and He-alth Management,2017: 7-13.
[36] ZHANG C,LIM P,QIN A K,et al.Multi objective deep belief networks ensemble for remaining useful life estimation in prognostics[J].Transactions on Neural Networks and Learning Systems,2016, 99: 1-13.
[38] BABU G S,ZHAO P,LI X L.Deep convolutional neural network based regression approach for estimation of remaining useful life[C]//The 21st International Conference on Database Systems for Advanced Ap-plications,2016: 214-228.
[39] SAXENA A,GOEBEL K.Turbofan engine degradation simulation data set[R].Moffett Field: NASA Ames Research Center,2008.
[40] SAXENA A,GOEBEL K.PHM08 challenge data set[R].Moffett Fi-eld: NASA Ames Research Center, 2008.
[41] YANG B,LIU R,ZIO E.Remaining useful life prediction based on a double convolutional neural network architecture[J].IEEE Transactions on Industrial Electronics, 2019,66(12): 9521-9530.
[42] ZHU J,CHEN N,PENG W.Estimation of bearing remaining useful life based on multiscale convolutional neural network[J].IEEE Transactions on Industrial Electronics,2019,66(4): 3208-3216.
[43] WEN L,DONG Y,GAO L.A new ensemble residual convolutional neu-ral network for remaining useful life estimation[J].Mathematical Biosciences and Engineering,2019,16: 862-880.
[45] LU P,MORRIS M,BRAZELL S,et al.Using generative adversarial net-works to improve deep-learning fault interpretation networks[J].The Leading Edge,2018,37(8): 578-583.
[46] MALHI A,YAN R,GAO R X.Prognosis of defect propagation based on recurrent neural networks[J].IEEE Transactions on Instrumentation and Measurement,2011,60(3): 703-711.
[48] HEIMES F O.Recurrent neural networks for remaining useful life estimation[C]//IEEE International Conference on Prognostics and He-alth Management,2008: 1-6.
[49] ZHAO R,WANG J,YAN R,et al.Machine health monitoring with LSTM networks[C]//The 10th International Conference on Sensing Technology,IEEE,2016: 1-6.
[50] YUAN M,WU Y,LIN L.Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network[C]//International Conference on Aircraft Utility Systems,IEEE,2016: 135-140.
[51] HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neu-ral Computation,1997,9(8): 1735-1780.
[52] CHOW T W S,FANG Y.A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics[J].IEEE Transactions on Industrial Electronics,1998,45(1): 151-161.
[53] ZHAO R,WANG D,YAN R,et al.Machine health monitoring using local feature based gated recurrent unit networks[J].IEEE Transactions on Industrial Electronics,2018, 65(2): 1539-1548.
[54] ZHOU G B,WU J,ZHANG C L,et al.Minimal gated unit for recurrent neural networks[J].International Journal of Automation and Computing,2016,13(3): 226-234.
[55] ZHANG B,ZHANG S,LI W.Bearing performance degradation assessment using long short-term memory recurrent network[J].Computers in Industry,2019,106: 14-29.
[56] WU Y,YUAN M,DONG S,et al.Remaining useful life estimation of engineered systems using vanilla LSTM neural networks[J].Neurocomputing,2018,275: 167-179.
[57] ZHENG S,RISTOVSKI K,FARAHAT A,et al.Long short-term memory network for remaining useful life estimation[C]//IEEE International Conference on Prognostics and Health Management,2017: 88-95.[58]ZHOU F,HU P,YANG X.RUL prognostics method based on real time updating of LSTM parameters[C]//Chinese Control and Decision Conference(CCDC), 2018: 3966-3971.
[58] ZHANG J,WANG P,YAN R,et al.Long short-term memory for machine remaining life prediction[J].Journal of Manufacturing Systems, 2018,48: 78-86.
[59] HUANG C G, HUANG H Z, LI Y F.A bidirectional LSTM prognostics method under multiple operational conditions[J].IEEE Tran-sactions on Industrial Electronics, 2019,66(11): 8792-8802.
[60] LI X,JIANG H,XIONG X,et al.Rolling bearing health prognosis using a modified health index based hierarchical gated recurrent unit network[J].Mechanism and Machine Theory,2019,133: 229-249.
[62] ALEXANDRE J,FREGNANI T G,DE MATTOS B S,et al.An innovative approach for integrated airline network and aircraft family optimization[J].Chinese Journal of Aeronautics,2020,33(2): 634-663.