Early warning method for heavy landing of civil aircraft based on real-time monitoring parameters

CAI Jing; CAI Kun-ye; HUANG Shi-jie

doi:10.19818/j.cnki.1671-1637.2022.02.024

Volume 22 Issue 2

Apr. 2022

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CAI Jing, CAI Kun-ye, HUANG Shi-jie. Early warning method for heavy landing of civil aircraft based on real-time monitoring parameters[J]. Journal of Traffic and Transportation Engineering, 2022, 22(2): 298-309. doi: 10.19818/j.cnki.1671-1637.2022.02.024

Citation:

CAI Jing, CAI Kun-ye, HUANG Shi-jie. Early warning method for heavy landing of civil aircraft based on real-time monitoring parameters[J]. Journal of Traffic and Transportation Engineering, 2022, 22(2): 298-309. doi: 10.19818/j.cnki.1671-1637.2022.02.024

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Early warning method for heavy landing of civil aircraft based on real-time monitoring parameters

doi: 10.19818/j.cnki.1671-1637.2022.02.024

1.
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China
2.
COMAC Shanghai Aircraft Customer Service Co., Ltd., Shanghai 200241, China

Funds:

National Natural Science Foundation of China 51705242

National Natural Science Foundation of China U1933202

More Information

Author Bio:
CAI Jing (1976-), male, associate professor, PhD, caijing@nuaa.edu.cn
Received Date: 2021-10-28
Publish Date: 2022-04-25

Abstract

Abstract

It was considered that the heavy landing events of civil aircraft can only be reported by pilots or checked passively by the maintenance personnels afterward at present, an early warning method for the heavy landing of civil aircraft based on real-time monitoring parameters was proposed. The influencing factors in heavy landing were analyzed, and on the basis of the preprocessed data of a quick access recorder (QAR), the grey relational analysis (GRA) was employed to extract 26 feature monitoring parameters from 52 monitoring parameters related to the heavy landing of aircraft. Taking the landing weight, vertical acceleration, vertical decreasing rate, and pitch rate as the prediction parameters and the 26 feature monitoring parameters as the inputs, a prediction model for the heavy landing of aircraft was built based on the long short-term memory (LSTM). The prediction model was trained with heavy landing cases, and the influence of the flight height range and the input/output step size on the prediction accuracy was analyzed to optimize the prediction model. The confusion matrix was introduced into the case verification to verify the prediction results of the model. Research results indicate that the LSTM-based prediction model can make use of the information that reflects the trend of heavy landing in the real-time monitoring data to realize early warning of heavy landing, the prediction accuracy of the model can reach 98% for 8 seconds of warning, and the average absolute error is only 0.018 3, which means the model can provide pilots adequate time margin to take measures to avoid the heavy landing. 6 tabs, 17 figs, 29 refs.
- civil aircraft,
- heavy landing,
- early warning,
- real-time monitoring,
- long short-term memory,
- grey relational analysis

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References(29)

References

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Early warning method for heavy landing of civil aircraft based on real-time monitoring parameters

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