Remaining useful life estimation in prognostics using deep convolution neural networks-Reference-Cited by-同舟云学术

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Remaining useful life estimation in prognostics using deep convolution neural networks

Published:2018-04 Issue: Volume:172 Page:1-11
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Li Xiang,Ding Qian,Sun Jian-Qiao

Funder

Northeastern University

National Science Foundation of China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Reference51 articles.

1. Condition-based maintenance effectiveness for series-parallel power generation system - a combined Markovian simulation model;Azadeh;Reliabil Eng Syst Saf,2015

2. Remaining useful life prediction of aircraft engine based on degradation pattern learning;Zhao;Reliabil Eng Syst Saf,2017

3. Prognostics and health management design for rotary machinery systems - reviews, methodology and applications;Lee;Mech Syst Signal Process,2014

4. Physics-of-failure-based prognostics for electronic products;Pecht;Trans Inst Meas Control,2009

5. Recurrent neural networks for remaining useful life estimation;Heimes,2008

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