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Accurate Remaining Useful Life Prediction with Uncertainty
  Quantification: a Deep Learning and Nonstationary Gaussian Process Approach

Accurate Remaining Useful Life Prediction with Uncertainty Quantification: a Deep Learning and Nonstationary Gaussian Process Approach

23 September 2021
Zhaoyi Xu
Yanjie Guo
J. Saleh
ArXivPDFHTML

Papers citing "Accurate Remaining Useful Life Prediction with Uncertainty Quantification: a Deep Learning and Nonstationary Gaussian Process Approach"

3 / 3 papers shown
Title
Machine Learning for Reliability Engineering and Safety Applications:
  Review of Current Status and Future Opportunities
Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities
Zhaoyi Xu
J. Saleh
62
345
0
19 Aug 2020
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
Markus Heinonen
Henrik Mannerstrom
Juho Rousu
Samuel Kaski
Harri Lähdesmäki
50
103
0
18 Aug 2015
Deep Gaussian Processes
Deep Gaussian Processes
Andreas C. Damianou
Neil D. Lawrence
GP
BDL
124
1,181
0
02 Nov 2012
1