A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings
Tài liệu tham khảo
Zhao, 2017, Remaining useful life prediction of aircraft engine based on degradation pattern learning, Reliab Eng Syst Saf, 10.1016/j.ress.2017.02.007
Azadeh, 2015, Condition-based maintenance effectiveness for series-parallel power generation system - A combined Markovian simulation model, Reliab Eng Syst Saf, 10.1016/j.ress.2015.04.009
Jouin, 2016, Degradations analysis and aging modeling for health assessment and prognostics of PEMFC, Reliab Eng Syst Saf, 10.1016/j.ress.2015.12.003
Ma, 2020, Deep wavelet sequence-based gated recurrent units for the prognosis of rotating machinery, Struct Heal Monit
Lei, 2016, A New Method Based on Stochastic Process Models for Machine Remaining Useful Life Prediction, IEEE Trans Instrum Meas, 65, 2671, 10.1109/TIM.2016.2601004
Lee, 2014, Prognostics and health management design for rotary machinery systems - Reviews, methodology and applications, Mech Syst Signal Process, 42, 314, 10.1016/j.ymssp.2013.06.004
Fink, 2015, A Classification Framework for Predicting Components’ Remaining Useful Life Based on Discrete-Event Diagnostic Data, IEEE Trans Reliab, 64, 1049, 10.1109/TR.2015.2440531
Zhu, 2020, A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions, Mech Syst Signal Process, 139, 10.1016/j.ymssp.2019.106602
Sakin, 2008, Statistical analysis of bending fatigue life data using Weibull distribution in glass-fiber reinforced polyester composites, Mater Des
Chen, 2020, Predictive maintenance using cox proportional hazard deep learning, Adv Eng Informatics, 10.1016/j.aei.2020.101054
Aremu, 2019, A Relative Entropy Weibull-SAX framework for health indices construction and health stage division in degradation modeling of multivariate time series asset data, Adv Eng Informatics, 10.1016/j.aei.2019.03.003
Caravaca, 2018, Impact of sandblasting on the mechanical properties and aging resistance of alumina and zirconia based ceramics, J Eur Ceram Soc, 10.1016/j.jeurceramsoc.2017.10.050
Ding, 2021, Stationary subspaces-vector autoregressive with exogenous terms methodology for degradation trend estimation of rolling and slewing bearings, Mech Syst Signal Process, 150, 10.1016/j.ymssp.2020.107293
Ding, 2020, A dynamic structure-adaptive symbolic approach for slewing bearings’ life prediction under variable working conditions, Struct Heal Monit
Kumar, 2013, ANN based evaluation of performance of wavelet transform for condition monitoring of rolling element bearing, Procedia Eng, 10.1016/j.proeng.2013.09.156
Tran, 2012, Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine, Mech Syst Signal Process, 10.1016/j.ymssp.2012.02.015
Fei, 2015, Kurtosis forecasting of bearing vibration signal based on the hybrid model of empirical mode decomposition and RVM with artificial bee colony algorithm, Expert Syst Appl, 10.1016/j.eswa.2014.11.047
Chen, 2021, Health indicator construction by quadratic function-based deep convolutional auto-encoder and its application into bearing RUL prediction, ISA Trans, 10.1016/j.isatra.2020.12.052
Zou, 2021, Fault diagnosis on the bearing of traction motor in high-speed trains based on deep learning, Alexandria Eng J, 10.1016/j.aej.2020.10.044
García Nieto, 2015, Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability, Reliab Eng Syst Saf, 138, 219, 10.1016/j.ress.2015.02.001
SONG, 2018, Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm, Chinese J Aeronaut, 31, 31, 10.1016/j.cja.2017.11.010
Ren, 2018, Bearing remaining useful life prediction based on deep autoencoder and deep neural networks, J Manuf Syst, 48, 71, 10.1016/j.jmsy.2018.04.008
Ye, 2018, A novel transfer learning framework for time series forecasting, Knowledge-Based Syst, 156, 74, 10.1016/j.knosys.2018.05.021
Mao, 2018, Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network, Adv Mech Eng, 10.1177/1687814018817184
Shi, 2021, A dual-LSTM framework combining change point detection and remaining useful life prediction, Reliab Eng Syst Saf, 205, 10.1016/j.ress.2020.107257
Ding, 2021, Remaining useful life estimation using deep metric transfer learning for kernel regression, Reliab Eng Syst Saf, 212, 10.1016/j.ress.2021.107583
Xiang, 2020, LSTM networks based on attention ordered neurons for gear remaining life prediction, ISA Trans, 10.1016/j.isatra.2020.06.023
Yu, 2019, Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme, Mech Syst Signal Process, 129, 764, 10.1016/j.ymssp.2019.05.005
Ma, 2021, Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction, IEEE Trans Ind Informatics
Chen, 2019, Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process, Reliab Eng Syst Saf, 10.1016/j.ress.2019.01.006
Caterini, 2018, Recurrent neural networks, SpringerBriefs Comput. Sci.
Lea, 2016, Temporal convolutional networks: A unified approach to action segmentation, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics)
Dai, 2020, Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data
Guo, 2020, Short-term traffic speed forecasting based on graph attention temporal convolutional networks, Neurocomputing, 10.1016/j.neucom.2020.06.001
Fu, 2015, Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals, Biomed Signal Process Control, 10.1016/j.bspc.2015.01.002
Yang, 2013, Trend extraction based on separations of consecutive empirical mode decomposition components in Hilbert marginal spectrum, Meas J Int Meas Confed, 10.1016/j.measurement.2013.04.071
Singh G, Yoon J, Son Y, Ahn S. Sequential neural processes. ArXiv 2019.
Bai S, Kolter JZ, Koltun V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. ArXiv 2018.
Vaswani, 2017, Attention is all you need, Adv. Neural Inf. Process. Syst.
Zhao, 2020, Exploring Self-attention for Image Recognition, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.
Chen, 2021, Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach, IEEE Trans Ind Electron
Liu, 2021, Remaining Useful Life Prediction Using a Novel Feature-Attention-Based End-to-End Approach, IEEE Trans Ind Informatics
Nectoux, 2012, PRONOSTIA : An experimental platform for bearings accelerated degradation tests, IEEE Int. Conf. Progn. Heal. Manag., 1
Wang, 2019, Deep separable convolutional network for remaining useful life prediction of machinery, Mech Syst Signal Process, 10.1016/j.ymssp.2019.106330
Zhu, 2019, Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network, IEEE Trans Ind Electron
Wang, 2020, A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings, IEEE Trans Reliab
Wang, 2020, Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery, Neurocomputing
