An improved aircraft hard landing prediction model based on panel data clustering

Qian, Silin; Zhou, Shenghan; Chang, Wenbing

doi:10.1109/ccdc.2017.7978134

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…Classifiers can be categorized into machine learning and deep learning approaches. Machine learning methods [17]- [19] apply a classifier to UAV flight data recorded using the Quick Access Recorder (QAR) sampled at a discrete set of heights that define the feature space. Most methods [17], [19] use the values of variables describing aircraft dynamics sampled between 9 and 2 meters before TD.…”

Section: Related Workmentioning

confidence: 99%

“…Machine learning methods [17]- [19] apply a classifier to UAV flight data recorded using the Quick Access Recorder (QAR) sampled at a discrete set of heights that define the feature space. Most methods [17], [19] use the values of variables describing aircraft dynamics sampled between 9 and 2 meters before TD. Others, like [18], use statistical descriptors (panel data) of such variables also sampled at the very last meters before TD.…”

Section: Related Workmentioning

confidence: 99%

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E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights

et al. 2022

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More than half of all commercial aircraft operation accidents could have been prevented by executing a go-around. Making timely decision to execute a go-around manoeuvre can potentially reduce overall aviation industry accident rate. In this paper, we describe a cockpit-deployable machine learning system to support flight crew go-around decision-making based on the prediction of a hard landing event. This work presents a hybrid approach for hard landing prediction that uses features modelling temporal dependencies of aircraft variables as inputs to a neural network. Based on a large dataset of 58177 commercial flights, the results show that our approach has 85% of average sensitivity with 74% of average specificity at the go-around point. It follows that our approach is a cockpit-deployable recommendation system that outperforms existing approaches.INDEX TERMS Decision support systems, hard landing prediction, machine learning, neural networks.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights

et al. 2022

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“…This work presents a hybrid approach for hard landing prediction that uses features modeling temporal dependencies of aircraft variables as inputs to a neural network. Qian [6] proposes a hard landing prediction method based on panel data clustering with flight data. The hard landing is a hazard that is critical to flight during the landing phase.…”

Section: Introductionmentioning

confidence: 99%

An advanced warning model of airplane hard landing based on Adaboost

Zhang

Liu

Huang

et al. 2023

J. Phys.: Conf. Ser.

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This paper breaks through the limitation of establishing early warning logic in the time dimension of the previous hard landing early warning model. In this research, flight altitude has been set as the scale of the corresponding early warning logic. According to the specific characteristics of the original flight parameter data set, the data preprocessing methods have been studied. The application of oversampling and frequency compression methods effectively solved the problems of different sample size and multi-frequency flight parameters in the original data set. On this basis, the method of ensemble learning is used to establish the early warning model of aircraft hard landing based on Adaboost. Further, the model is optimized to ensure the accuracy and generalization ability of the early warning model. Verified by the case study of the actual flight parameter data of Airbus A320 aircraft, the early warning model can realize high-precision prediction of hard landing events, which is of great significance for the improvement of the landing safety level of the aircraft in constructing both.

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“…Moreover, previous studies on UA primarily focused on artificial [11], meteorological, and mechanical [12] influencing factors, such as the impact of sudden low-altitude wind shear [13]. Risks potentially caused by UA have also been widely evaluated via the gray clustering method [14], approaching angle and trajectory analysis [15], K-means clustering [13], and neural networks [16]. However, UA events and relative factors have rarely been studied from spatial and temporal perspectives [17,18].…”

Section: Introductionmentioning

confidence: 99%

Event Detection and Spatio-temporal Analysis of Low-Altitude Unstable Approach

et al. 2020

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Low-altitude unstable approach (UA) is one of the crucial risks that threaten flight safety. In this study, we proposed a technical program for detecting low-altitude UA events. The detection logic was to optimize the step-wise regression model with iterative surveys with more than 20 experienced pilots. Accordingly, the frequencies of UA events occurring around each airport in January 2018 were calculated for all the airports within mainland China. Finally, the spatial distribution characteristics of UA events were analyzed via exploratory spatial data analysis. In addition, Pearson’s correlation coefficient and the geographically weighted correlation coefficient were used to explore the correlations between UA frequency and the altitude elevation, wind level, and bad weather. The experimental results revealed that the proposed method can accurately detect the occurrence of low-altitude UA and quantitatively characterize risks. It was found that UA exhibits obvious differences in spatial distribution. Moreover, significantly strong correlations were found between UA and altitude elevation, wind level, and bad weather, and correlation differences were also reflected in different regions in China.

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An improved aircraft hard landing prediction model based on panel data clustering

Cited by 10 publications

References 4 publications

E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights

E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights

An advanced warning model of airplane hard landing based on Adaboost

Event Detection and Spatio-temporal Analysis of Low-Altitude Unstable Approach

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