A FP-CNN method for aircraft fault prognostics
- https://doi.org/10.2991/amcce-18.2018.99How to use a DOI?
- fault prognosis convolutional neural network (FP-CNN), aircraft fault prognostics
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.
- © 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/).
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 - 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 - https://doi.org/10.2991/amcce-18.2018.99 ID - Chen2018/05 ER -