@inproceedings{6a4ed43a84364db79b2ad4d63209d1e9,
title = "Performance parameter estimation of aircraft auxiliary power unit via a fusion model",
abstract = "The Auxiliary Power Unit (APU) is designed to provide power and compressed air to the aircraft independently. By estimating the performance parameter of APU, its potential failure and abnormal information can be perceived in advance. To obtain accurate estimation result, Long Short-Term Memory (LSTM) network and Support Vector Regression (SVR) model are fused by Kalman Filter (KF). In this approach, LSTM network model is used as the state equation and SVR model is used as the observation equation. The effectiveness of this method is verified by adopting the real data of APU from the China Southern Airlines Company Limited Shenyang Maintenance Base.",
keywords = "Auxiliary power unit, Kalman filter, Long short-term memory network, Support vector regression",
author = "Xiaolei Liu and Zhigang Li and Lulu Wang and Liansheng Liu and Xiyuan Peng",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019 ; Conference date: 15-08-2019 Through 17-08-2019",
year = "2019",
month = aug,
doi = "10.1109/SDPC.2019.00100",
language = "英语",
series = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "523--528",
editor = "Chuan Li and Shaohui Zhang and Jianyu Long and Diego Cabrera and Ping Ding",
booktitle = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",
address = "美国",
}