@inproceedings{8c81e51a60c94e0fa6b3d8606227d256,
title = "Aircraft fault diagnosis based on deep belief network",
abstract = "It is a great challenge to accurately and automatically diagnose different faults of aircraft using traditional method. In this paper, a new method based on deep belief network is proposed for aircraft key parts fault diagnosis. Firstly, a deep belief network is constructed with a series of pre-Trained restricted Boltzmann machines for feature learning. Secondly, the highest level features learned from the DBN are fed into a softmax classifier for fault diagnosis. Finally, back-propagation learning algorithm is adopted to fine-Tune the deep model parameters to further improve the diagnosis accuracy. The proposed method is applied to analyze the experimental rolling bearing signals. The results show that the proposed method is more effective and robust than other traditional methods.",
keywords = "Aircraft, Deep belief network, Fault diagnosis, Restricted boltzmann machine",
author = "Hongkai Jiang and Haidong Shao and Xinxia Chen and Jiyang Huang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017 ; Conference date: 16-08-2017 Through 18-08-2017",
year = "2017",
month = dec,
day = "9",
doi = "10.1109/SDPC.2017.32",
language = "英语",
series = "Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "123--127",
editor = "Wei Guo and {de Oliveira}, {Jose Valente} and Chuan Li and Yun Bai and Ping Ding and Juanjuan Shi",
booktitle = "Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017",
}