A Deep learning Method for Landing Pitch Prediction based on Flight Data

Chen, Hongnian; Shang, Jiaxing; Zhao, Xianfeng; Li, Xu; Zheng, Linjiang; Chen, Fengzhang

doi:10.1109/iccasit50869.2020.9368623

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…With the rise of deep learning and in order to capture time-series features, Tong et al [9] proposed a model based on the Long Short-Term Memory (LSTM) network to predict a hard landing. The same model was also used to address the landing speed prediction problem [19] and the tail strike risk prediction problem [20]. Kang et al [21] further proposed a deep sequence-tosequence model based on LSTM and an attention mechanism to improve the landing speed prediction accuracy.…”

Section: Safety Incident Predictionmentioning

confidence: 99%

An Unsupervised Learning Approach for Analyzing Unsafe Pilot Operations Based on Flight Data

Qian

Chen

et al. 2022

Applied Sciences

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Flight safety is a hot topic in the aviation industry. Statistics show that safety incidents during landing are closely related to the flare phase because this critical period requires extensive pilot operations. Many airlines require that pilots should avoid performing any forward stick inputs during the flare. However, our statistical results from about 86,504 flights show that this unsafe pilot operation occasionally happens. Although several case studies were conducted previously, systematic research, especially based on a large volume of flight data, is still missing. This paper aims to fill this gap and provide more insights into the issue of pilots’ unsafe stick operations during the flare phase. Specifically, our work is based on the Quick Access Recorder (QAR) data, which consist of multivariate time-series data from various flight parameters. The raw data were carefully preprocessed, then key features were extracted based on flight expert experience, and a K-means clustering algorithm was utilized to divide the unsafe pilot operations into four categories. Based on the clustering results, we conducted an in-depth analysis to uncover the reasons for different types of unsafe pilot stick operations. In addition, extensive experiments were conducted to further investigate how these unsafe operations are correlated with different factors, including airlines, airports, and pilots. To the best of our knowledge, this is the first systematic study analyzing pilots’ unsafe forward stick operations based on a large volume of flight data. The findings can be used by airlines to design more targeted pilot training programs in the future.

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Section: Safety Incident Predictionmentioning

confidence: 99%

An Unsupervised Learning Approach for Analyzing Unsafe Pilot Operations Based on Flight Data

Qian

Chen

et al. 2022

Applied Sciences

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“…This method was able to identify events from the multidimensional time series that are correlated with the high-speed exceedance incidents. Chen et al [21] proposed a LSTM deep model for landing pitch prediction so as to reduce the tail strike risk. One deficiency of the LSTM and GRU models is that they only generate predictions for the next moment and cannot provide predictions for the trend of near future.…”

Section: Related Workmentioning

confidence: 99%

“…Chen et al. [21] proposed a LSTM deep model for landing pitch prediction so as to reduce the tail strike risk. One deficiency of the LSTM and GRU models is that they only generate predictions for the next moment and cannot provide predictions for the trend of near future.…”

Section: Related Workmentioning

confidence: 99%

A deep sequence‐to‐sequence method for accurate long landing prediction based on flight data

Kang

Shang

Feng

et al. 2021

IET Intelligent Trans Sys

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In civil aviation industry, runway overrun is a typical landing safety incident concerned by both airlines and authorities. Among various contributing factors to the runway overrun incident, long landing plays an important role. However, existing studies for long landing prediction mainly depend on classic machine learning methods and handcrafted features. As a result, they usually require much expert knowledge and provide unsatisfactory results. To address these problems, this paper proposes an innovative deep sequence-to-sequence model which utilizes QAR (Quick Access Recorder) data for accurate long landing pre-diction. Specifically, to cope with the high heterogeneity of QAR dataset, a data pre-processing procedure is first proposed which includes data cleaning, interpolation and normalization steps. Second, to avoid the noises incurred by too many QAR parameters and relieve the reliance on expert experience, the GBDT (gradient boosting decision trees) model is employed to choose the most relevant parameters as features. Then a CNN-LSTM and TG-attention encoder-decoder architecture is proposed to accurately predict future aircraft ground speed and radio height sequences, based on which the touchdown distance can be finally calculated. Experimental results on a large QAR dataset with 44,176 A321 flights validate effectiveness of the proposed method. INTRODUCTIONIn civil aviation industry, flight safety is a vital important issue concerned by both airlines and authorities. According to the Boeing statistics [1], the landing phase, which only occupies 4% of the overall flight time, contributes to 48% of the fatal accidents during 2007 to 2016. This phase also has the largestThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…In particular, structural data is the main subject of current research, and the processing and analysis of QAR data is a top priority of current research due to the advantages of completeness and reliability of QAR data. In this regard, related scholars [22][23][24] have proposed using an LSTM model to analyze QAR data for predictive evaluation of the associated risks. After that, affiliated scholars made continuous improvements based on LSTM.…”

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confidence: 99%

An Innovative Deep Architecture for Flight Safety Risk Assessment Based on Time Series Data

Sun,

Yang,

Zhang

et al. 2024

CMES

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With the development of the integration of aviation safety and artificial intelligence, research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management, but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry. Therefore, an improved risk assessment algorithm (PS-AE-LSTM) based on long short-term memory network (LSTM) with autoencoder (AE) is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels. Firstly, based on the normal distribution characteristics of flight data, a probability severity (PS) model is established to enhance the quality of risk assessment labels. Secondly, autoencoder is introduced to reconstruct the flight parameter data to improve the data quality. Finally, utilizing the time-series nature of flight data, a long and short-term memory network is used to classify the risk level and improve the accuracy of risk assessment. Thus, a risk assessment experiment was conducted to analyze a fleet landing phase dataset using the PS-AE-LSTM algorithm to assess the risk level associated with aircraft hard landing events. The results show that the proposed algorithm achieves an accuracy of 86.45% compared with seven baseline models and has excellent risk assessment capability. KEYWORDSSafety engineering; risk assessment; time series data; autoencoder; LSTM Nomenclature LSTM Long short-term memory network PS-AE-LSTM Improved risk assessment algorithm AE Autoencoder PS Probability severity VANETs Vehicular Ad-hoc networks

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A Deep learning Method for Landing Pitch Prediction based on Flight Data

Cited by 4 publications

References 9 publications

An Unsupervised Learning Approach for Analyzing Unsafe Pilot Operations Based on Flight Data

An Unsupervised Learning Approach for Analyzing Unsafe Pilot Operations Based on Flight Data

A deep sequence‐to‐sequence method for accurate long landing prediction based on flight data

An Innovative Deep Architecture for Flight Safety Risk Assessment Based on Time Series Data

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