General Aviation Aircraft Flight Operations Quality Assurance: Overcoming the Obstacles

Mitchell, Kyle; Sholy, B.; Stolzer, Alan J.

doi:10.1109/maes.2007.384075

Cited by 15 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During a flight, hundreds to thousands of flight parameters are recorded in QAR data, such as altitude, airspeed, pitch angle, roll angle, engine parameters, and control surface position. Lots of aviation safety experts have devoted to studying factors of pilot maneuvering by using QAR data [15], especially on specific events. In 2014, Wang et al discovered that the period of 200 feet to touchdown and the flare action are key features of long landing events [16].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The flight operation quality assurance (FOQA) program, also known as flight data monitoring in Europe, strives to improve airline safety by utilizing flight data [15]. However, because the exploration of QAR data is still focalized on detecting exceedances, following approaches can only expose a minority of the information buried in the data [20].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Pilot Maneuvering Performance Analysis and Evaluation with Deep Learning

Zhang¹,

Huo²,

Sun³

et al. 2023

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Safety is the prerequisite for the steady development of all industries and the basis for the survival of the transportation sector. Ensuring aviation safety has always been a major challenge for aviation activities [1].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in QAR Data Using VAE-LSTM with Multihead Self-Attention Mechanism

Rong

OuYang

Sun

2022

Mobile Information Systems

View full text Add to dashboard Cite

With the rapid development of the aviation industry, it is particularly important to ensure the safe flight of aircraft. How to find potential hazards in the process of aircraft flight has always been one of the important topics of civil aviation research. At present, the Quick Access Recorder (QAR) is the most widely used equipment to store the data recorded on aircraft. QAR data contain a lot of valuable and unexplored information, which records the true status of the aircraft in detail. Therefore, finding abnormal data from QAR data lays an important foundation for obtaining the cause of abnormality and providing a guarantee for flight. In this paper, in order to discover the abnormal information in the QAR data, we applied a VAE-LSTM model with a multihead self-attention mechanism. Compared to the VAE and LSTM models alone, our model performs much better in anomaly detection and prediction, detecting all types of anomalies. We conducted extensive experiments on real-world QAR data sets to prove the efficiency and accuracy of our proposed neural network model. The experimental results proved that our proposed model can outperform state-of-the-art models under different experimental settings.

show abstract

“…Actually, QAR data have gradually been applied to flight operations diagnosis. The implementations of Flight Operations Quality Assurance (FOQA) program into the general aviation industry were fully promoted (Mitchell et al , 2007). With QAR data, some researchers established neural networks to analyze causes and severity of flight exceedances such as hard landing, touchdown point further (Cao et al , 2008; Shao et al , 2011).…”

Section: Introductionmentioning

confidence: 99%

A risk analysis model of flight operations based on region partition

Shao¹,

Qi²,

Gao³

2012

Kybernetes

View full text Add to dashboard Cite

Purpose -The purpose of this paper is to focus on flight exceedances in pilots' operations. With some bad conditions, such as a bad weather, flight exceedances might lead to serious consequences. They are significant hidden dangers of aviation. Risk analysis is carried out to identify pilots' high-risk or low-risk operations. Design/methodology/approach -A multi-objective optimization model is proposed for risk analysis of flight operations. An evolutionary algorithm is designed to divide flight operation state-space into some high-risk and low-risk sub-spaces. Findings -Through the empirical study of a certain flight exceedance with the analysis model, the authors discover some high-risk flight operations, which indicate coordination problems in coordinate control of airplane's speed, rate of descent, heading, roll and pitch, etc. Originality/value -This paper employs a quantitative model to carry out risk analysis of flight operations. The results are useful to pilots' training and may improve flight safety fundamentally. The risk analysis of flight exceedance is one specific case in airlines safety risk management. Some other problems, such as cause analysis of flight delay, aircraft faults diagnosis, can be addressed in the same way and dealt with by specific model adjustments and algorithm designs.

show abstract

General Aviation Aircraft Flight Operations Quality Assurance: Overcoming the Obstacles

Cited by 15 publications

References 0 publications

Pilot Maneuvering Performance Analysis and Evaluation with Deep Learning

Pilot Maneuvering Performance Analysis and Evaluation with Deep Learning

Anomaly Detection in QAR Data Using VAE-LSTM with Multihead Self-Attention Mechanism

A risk analysis model of flight operations based on region partition

Contact Info

Product

Resources

About