A FP-CNN method for aircraft fault prognostics

DOI

10.2991/amcce-18.2018.99How to use a DOI?

Keywords

fault prognosis convolutional neural network (FP-CNN), aircraft fault prognostics

Abstract

Predicting the status of flight vehicle in advance can have huge advantages in maintenance and early warning areas. Accurate forecast helps reduce maintenance costs and improve safety during the aircraft's life cycle. Combining the ability of convolutional neural network to extract features of different levels and its computational efficiency, a novel convolutional neural network -- fault prognosis convolutional neural network(FP-CNN) is proposed in this paper, the purpose of which is to predict the Remaining Useful Life (RUL) by learning sequential information and extracting sensor features from noisy datasets under different operating modes. An experiment on CMPASS data is conducted to prove the efficiency and accuracy of this framework.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhiyu Chen
AU  - Lihong Shang
AU  - Mi Zhou
PY  - 2018/05
DA  - 2018/05
TI  - A FP-CNN method for aircraft fault prognostics
BT  - Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
PB  - Atlantis Press
SP  - 571
EP  - 579
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-18.2018.99
DO  - 10.2991/amcce-18.2018.99
ID  - Chen2018/05
ER  -