Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics

IEEE Transactions on Neural Networks and Learning Systems - Tập 28 Số 10 - Trang 2306-2318 - 2017
Chong Zhang1, Pin Lim2, A. K. Qin3, Kay Chen Tan1
1Department of Electrical and Computer Engineering, National University of Singapore, Singapore
2Advance Technology Center of Rolls Royce Singapore, Singapore
3School of Science, RMIT University, Melbourne, VIC, Australia

Tóm tắt

Từ khóa


Tài liệu tham khảo

10.1023/B:STCO.0000035301.49549.88

10.1016/j.neucom.2005.12.126

ramasso, 2014, Review and analysis of algorithmic approaches developed for prognostics on CMAPSS dataset, Proc Annu Conf Prognostics Health Manage Soc (PHM), 612

10.1109/PHM.2008.4711436

ho, 1998, The random subspace method for constructing decision forests, IEEE Trans Pattern Anal Mach Intell, 20, 832, 10.1109/34.709601

tamilselvan, 2012, Deep belief network based state classification for structural health diagnosis, Proc IEEE Aerosp Conf, 1

tibshirani, 1996, Regression shrinkage and selection via the lasso, J Roy Statist Soc Series B (Methodol ), 58, 267, 10.1111/j.2517-6161.1996.tb02080.x

10.1109/TNNLS.2013.2264952

10.1214/aos/1013203451

10.1109/TNNLS.2013.2246578

10.1016/j.ress.2013.02.022

10.1016/j.neucom.2013.03.047

10.1109/TIE.2010.2098369

10.1109/TCYB.2014.2378056

riad, 2010, Evaluation of neural networks in the subject of prognostics as compared to linear regression model, Int J Eng Technol, 10, 50

10.1109/PHM.2008.4711423

10.1109/TNNLS.2013.2268279

10.1016/j.inffus.2004.04.004

10.1109/ICPHM.2012.6299526

10.1109/ICPHM.2013.6621419

hinton, 2010, A practical guide to training restricted Boltzmann machines, Momentum, 9, 926

10.1109/TKDE.2010.26

10.1109/4235.887237

10.1109/TEVC.2008.925798

10.1016/j.ymssp.2005.11.008

10.1109/TEVC.2007.892759

10.1109/TIE.2004.824875

saxena, 2008, Turbofan Engine Degradation Simulation Data Set

10.1109/SMC.2015.19

10.1016/j.ymssp.2007.12.004

10.1109/PHM.2008.4711414

10.1109/PHM.2008.4711456

10.1016/j.ymssp.2011.10.019

10.1115/1.4004981

10.1109/TR.2012.2196171

10.1007/s11047-005-1625-y

10.21236/AD0256582

10.1162/neco.2006.18.7.1527

10.1109/CEC.2013.6557689

sutton, 1986, Two problems with backpropagation and other steepest-descent learning procedures for networks, Proc 8th Annu Conf Cognit Sci Soc, 823

10.1016/j.ress.2013.08.004

10.1016/j.ress.2010.02.016

10.1109/TEVC.2015.2443001

10.1016/S0893-6080(99)00073-8

10.1016/j.neunet.2014.09.003

10.1109/ICASSP.2011.5947494

10.1109/TASL.2011.2109382

10.1145/1553374.1553453

10.1109/TNNLS.2013.2296046

10.1109/AERO.2014.6836267

10.1109/CEC.2003.1299928

10.1007/3-540-45656-2_1

10.1016/j.jpowsour.2004.02.032

10.1016/j.ress.2009.08.001

dietterich, 1997, Machine-learning research, AI Mag, 18, 97

10.1109/AERO.2010.5446841

10.1007/978-1-4020-6668-9_5

10.1016/j.ress.2012.03.008

lim, 2014, Estimation of remaining useful life based on switching Kalman filter neural network ensemble, Proc Int Conf Prognostics Health Manage (PHM), 2

10.1145/2330163.2330301

lim, 2016, Multimodal degradation prognostics based on switching Kalman filter ensemble, IEEE Trans Neural Netw Learn Syst, pp, 1

10.1016/j.ymssp.2013.07.010

10.1023/A:1008202821328

10.1109/TEVC.2008.927706

10.1109/MCI.2016.2532268

10.1109/TEVC.2015.2433672

10.1109/JSEN.2013.2293517

10.1109/PHM.2008.4711422

10.1177/0142331208092031

10.1177/0142331208092030

10.1016/j.ymssp.2008.06.009

10.1109/PHM.2008.4711421

10.1016/j.ymssp.2009.11.005

10.1109/TEVC.2015.2455812

10.2514/6.2005-7002

10.1109/TEVC.2014.2353672

10.1109/TEVC.2014.2308305

10.1109/TEVC.2015.2450018

10.1109/TEVC.2014.2301794

krogh, 1995, Neural network ensembles, cross validation, and active learning, Proc Adv Neural Inf Process Syst, 231

10.1109/34.58871

10.1007/s10852-005-9020-3

opitz, 1999, Popular ensemble methods: An empirical study, J Artif Intell Res, 11, 169, 10.1613/jair.614