Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)

A FP-CNN method for aircraft fault prognostics

Authors
Zhiyu Chen, Lihong Shang, Mi Zhou
Corresponding Author
Zhiyu Chen
Available Online May 2018.
DOI
https://doi.org/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.
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Proceedings
2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
Part of series
Advances in Engineering Research
Publication Date
May 2018
ISBN
978-94-6252-508-5
ISSN
2352-5401
DOI
https://doi.org/10.2991/amcce-18.2018.99How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-18.2018.99
DO  - https://doi.org/10.2991/amcce-18.2018.99
ID  - Chen2018/05
ER  -