TY - GEN
T1 - A recurrent neural network based method for predicting the state of aircraft air conditioning system
AU - Zhang, Yuxuan
AU - Li, Yuanxiang
AU - Wei, Xian
AU - Peng, Xishuai
AU - Zhao, Honghua
AU - Shen, Kaijie
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - The reliability and safety of aircraft has always been the focus of research attention. As an important component of the aircraft, the air conditioning system has a direct impact on the safety of the flight process. In order to ensure the safety of the flight process, this paper proposes a recurrent neural network (RNN) based method for predicting the working state of the aircraft air conditioning system. Using the measured data collected from the Boeing 737NG aircraft, we train a RNN and experimental results on short-term prediction show that our proposed method can obtain a good prediction accuracy. In addition, we modify the network to make longer prediction using a bidirectional architecture. The experimental results on long-term prediction show that this network can solve the problem that the prediction results at first several seconds are much larger than the actual measured value and can learn a good representation for the time series.
AB - The reliability and safety of aircraft has always been the focus of research attention. As an important component of the aircraft, the air conditioning system has a direct impact on the safety of the flight process. In order to ensure the safety of the flight process, this paper proposes a recurrent neural network (RNN) based method for predicting the working state of the aircraft air conditioning system. Using the measured data collected from the Boeing 737NG aircraft, we train a RNN and experimental results on short-term prediction show that our proposed method can obtain a good prediction accuracy. In addition, we modify the network to make longer prediction using a bidirectional architecture. The experimental results on long-term prediction show that this network can solve the problem that the prediction results at first several seconds are much larger than the actual measured value and can learn a good representation for the time series.
KW - RNN
KW - aircraft air conditioning system
KW - bidirectional architecture
KW - prediction
UR - https://www.scopus.com/pages/publications/85046101747
U2 - 10.1109/SSCI.2017.8285339
DO - 10.1109/SSCI.2017.8285339
M3 - 会议稿件
AN - SCOPUS:85046101747
T3 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
SP - 1
EP - 7
BT - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Y2 - 27 November 2017 through 1 December 2017
ER -