Name
Abstract | ||
|---|---|---|
The Quick Access Recorder (QAR) is an airborne data recording device used to collect and record a large amount of operational data generated by various systems in the aircraft. Aircraft engine fuel flow is an important data recorded by QAR. Modeling fuel flow is of great significance for airlines to monitor the operating status of aeroengine, guide pilot training and QAR data management. In order to establish the fuel flow model of the aircraft, this paper obtains the aircraft's flight parameters, aircraft attitude, control signals, engines, environment (wind and temperature) and other QAR data to construct feature vectors, and uses the weighted average of the two-engine fuel flow as the fitting target. LSTM is used to build regression relationship model between feature vector and fitting target, and the validity and accuracy of the model are verified by real flight data. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ISCID51228.2020.00033 | 2020 13th International Symposium on Computational Intelligence and Design (ISCID) |
Keywords | DocType | ISSN |
aircraft data mining,QAR,fuel flow model,machine learning,neural networks,LSTM | Conference | 2165-1701 |
ISBN | Citations | PageRank |
978-1-7281-8447-0 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weizhen Luo | 1 | 0 | 0.34 |
| Zixuan Wu | 2 | 0 | 0.34 |
| Cong Chen | 3 | 38 | 12.03 |