Remaining useful life estimation in prognostics using deep convolution neural networks

•Propose a novel deep convolutional neural network-based method for remaining useful life predictions.•No prior expertise on prognostics and signal processing is required, that facilitates the application of the proposed method.•Effects of the key factors on the prognostic performance are widely inv...

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Bibliographic Details
Published in:	Reliability engineering & system safety Vol. 172; pp. 1 - 11
Main Authors:	Li, Xiang, Ding, Qian, Sun, Jian-Qiao
Format:	Journal Article
Language:	English
Published:	Barking Elsevier Ltd 01.04.2018 Elsevier BV
Subjects:	Components Convolution Convolution neural network Critical components Data processing Deep learning Feature extraction Information processing Neural networks Prognostics and health management Reliability engineering Remaining useful life Sample preparation Signal processing Studies Useful life Windows (intervals) Deep learning Remaining useful life Convolution neural network Prognostics and health management
ISSN:	0951-8320, 1879-0836
Online Access:	Get full text
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