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

Kang, Zongwei; Shang, Jiaxing; Feng, Yong; Zheng, Linjiang; Wang, Qixing; Qiang, Baohua

doi:10.1049/itr2.12078

Cited by 12 publications

(4 citation statements)

References 34 publications

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“…The results showed that the main causal factors were derived from low vertical path and late flare. In the same year, Kang et al also proposed an innovative deep sequence-to-sequence model to estimate landing distance [19]. Experiments on 44,176 Airbus 321 flights showed that the error between the real and predicted landing distance is 26 meters.…”

Section: Literature Reviewmentioning

confidence: 99%

Pilot Maneuvering Performance Analysis and Evaluation with Deep Learning

Zhang¹,

Huo²,

Sun³

et al. 2023

International Journal of Aerospace Engineering

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Quick access recorder (QAR) data have been used to evaluate pilot performance for decades. However, traditional evaluation methods suffer from the inability to consider multiple parameters simultaneously, and most of them need to select features manually in advance. To study the relationship between QAR data and pilot performance, this paper puts forward one-dimensional convolutional neural networks (1-D CNN) which consider QAR metrics in an integrated manner. This paper obtained indicators describing the operational status of an aircraft first. Then, the correlation between indicators and pilot performance (skill levels) was studied. Inspired by the fact that CNN can extract both local and global features, this paper has developed an approach to achieve the state-of-the-art result in pilot performance evaluation, which was tested on operating data of Boeing 737. The results prove that other methods do not work well, while the 1-D CNN improves the prediction accuracy of 5 pilot skill levels. Besides, when it is used on a binary classification problem, the result improves to 78.18%. Finally, the indicators were grouped into 5 common factors by factor analysis and fed into 1-D CNN in different combinations. Each common factor plays a different role in pilot performance evaluation, which can provide advice for the future.

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Section: Literature Reviewmentioning

confidence: 99%

Pilot Maneuvering Performance Analysis and Evaluation with Deep Learning

Zhang¹,

Huo²,

Sun³

et al. 2023

International Journal of Aerospace Engineering

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“…Subsequently, Wang et al [15,16] proposed a runway overrun risk assessment model based on QAR data, defining the long landing risk as the product of the probability of a certain landing distance and the severity of risk corresponding to that landing distance. Kang et al [17,18] investigated the long landing problem and proposed a deep sequence-to-sequence model to predict the landing speed and distance. Lv et al [19] defined runway overrun risk as a function related to the remaining runway distance and touchdown speed, dividing flights into high-risk and low-risk flights based on the magnitude of the risk indicator.…”

Section: Runway Overrunmentioning

confidence: 99%

A Runway Overrun Risk Assessment Model for Civil Aircraft Based on Quick Access Recorder Data

Li,

Zhang,

Shang

et al. 2023

Applied Sciences

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The quick access recorder (QAR), as an airborne device used to monitor and record flight parameters, has been widely installed on various types of aircraft. Based on QAR data, research on runway overrun, a typical flight safety incident, has attracted widespread attention in recent years. However, existing runway overrun risk models generally suffer from oversimplified risk metrics or insufficient consideration of risk dynamics. In this paper, we propose a new dynamic runway overrun risk assessment model based on QAR data. We first consider the noise of aircraft trajectory data in the QAR parameters and present a landing trajectory correction method combining ground speed and runway position information. Second, to improve the accuracy of the risk assessment model, we design an algorithm to automatically recognize the aircraft autobrake level during the landing phase, based on which a new dynamic risk assessment model is developed. Finally, feature engineering is performed to extract the relevant contributing factors of runway overrun risk, based on which classification and regression models are applied to identify risky flights and predict the risk values. The proposed risk assessment model was evaluated using QAR data from an airline in China. The results show that the automatic deceleration rate, the way that the aircraft approaches the runway, the touchdown distance, and the kinetic energy at 50 ft are key factors in the risk of runway overrun during the landing phase.

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“…Similarly, for the long landing incident, Wang et al [22] investigated the correlation between different QAR parameters and a long landing through the analysis of variance method and utilized the logical regression and linear regression models for the long landing risk prediction. Recently, Kang et al [23] utilized a deep sequence-to-sequence model for long landing prediction, which further improved the prediction accuracy by incorporating an attention mechanism. Predicting aviation safety incidents can enable proactive warnings before safety incidents occur, but these methods cannot help uncover the reasons for safety incidents.…”

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

Cited by 12 publications

References 34 publications

Pilot Maneuvering Performance Analysis and Evaluation with Deep Learning

Pilot Maneuvering Performance Analysis and Evaluation with Deep Learning

A Runway Overrun Risk Assessment Model for Civil Aircraft Based on Quick Access Recorder Data

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

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