Reconstruction of Aircraft States During Landing Based on Quick Access Recorder Data

Höhndorf, Lukas; Siegel, Joachim; Sembiring, Javensius; Koppitz, Phillip; Holzapfel, Florian

doi:10.2514/1.g002637

Cited by 12 publications

(5 citation statements)

References 4 publications

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“…A preprocessing of the data is implemented using the Rauch-Tung-Striebel smoother smoother to improve the data quality. For the details of the data smoothing, readers are referred to reference given in [ 24 ]. Modeling of headwind data during the final approach is presented, followed by another case study of the horizontal wind shear ramps construction.…”

Section: Results and Discussion Of Wind Process Modelingmentioning

confidence: 99%

Modeling of Stochastic Wind Based on Operational Flight Data Using Karhunen–Loève Expansion Method

Wang

Beller

Czado

et al. 2020

Sensors

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Wind has a significant influence on the operational flight safety. To quantify the influence of the wind characteristics, a wind series generator is required in simulations. This paper presents a method to model the stochastic wind based on operational flight data using the Karhunen–Loève expansion. The proposed wind model allows us to generate new realizations of wind series, which follow the original statistical characteristics. To improve the accuracy of this wind model, a vine copula is used in this paper to capture the high dimensional dependence among the random variables in the expansions. Besides, the proposed stochastic model based on the Karhunen–Loève expansion is compared with the well-known von Karman turbulence model based on the spectral representation in this paper. Modeling results of turbulence data validate that the Karhunen–Loève expansion and the spectral representation coincide in the stationary process. Furthermore, construction results of the non-stationary wind process from operational flights show that the generated wind series have a good match in the statistical characteristics with the raw data. The proposed stochastic wind model allows us to integrate the new wind series into the Monte Carlo Simulation for quantitative assessments.

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Section: Results and Discussion Of Wind Process Modelingmentioning

confidence: 99%

Modeling of Stochastic Wind Based on Operational Flight Data Using Karhunen–Loève Expansion Method

Wang

Beller

Czado

et al. 2020

Sensors

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“…The physical model that is the basis for the ideas and methods considered within this paper was mainly developed within [2] and [3] and also summarized in [4] and [1]. It is specifically designed for application during the landing phase of the aircraft.…”

Section: Physical Aircraft Model For Landing Reconstructionmentioning

confidence: 99%

Statistical Dependence Analyses of Operational Flight Data Used for Landing Reconstruction Enhancement

Höhndorf¹,

Nägler²,

Koppitz³

et al. 2022

Preprint

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To analyze flight data recorded during an airline's flight operation, a so-called Rauch-Tung-Striebel (RTS) smoother is implemented at the Institute of Flight System Dynamics. The RTS smoother is widely used for state estimation and it is utilized here to increase the data quality with respect to physical coherence and to increase resolution. The purpose of this paper is to enhance the performance of the RTS smoother to reconstruct an aircraft landing using on board recorded data only. Thereby, errors and uncertainties of operational flight data (e.g. altitude, attitude, position, speed) recorded during flights of civil aircraft are minimized. These data can be used for subsequent analyses in terms of flight safety or efficiency, which is commonly referred to as Flight Data Monitoring (FDM).Statistical assumptions of the smoother theory are not always verified during application but (consciously or not) assumed to be fulfilled. These assumptions can hardly be verified prior to the smoother application, however, they can be verified using the results of an initial smoother iteration and modifications of specific smoother characteristics can be suggested. This project specifically verifies assumptions on the measurement noise characteristics.Variance and covariance of the measurement noise can be checked after the initial smoother application. It is discovered that these characteristics change over time and should be accounted for with a time varying covariance matrix. This sequence of matrices is estimated by kernel smoothing and replaces an initially assumed fixed and diagonal covariance matrix used for the first smoother run. The results of this second smoother iteration are mostly improved compared to the initial iteration, i.e. the errors are significantly reduced. Subsequently, the remaining dependence structures of the residuals of the second smoother iteration can be captured by copula models. Their interpretation is useful for a revision of the physical model utilized by the RTS smoother.

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“…Diverse data analysis systems are developed to explore operational risks, evaluate and assess pilot flight technology, identify technical training problems, optimize training processes, enhance flight training quality, monitor flight quality, engine status and elevate the underutilized QAR data rate (Haverdings and Chan, 2010; Sun, 2003; Li, 2010). Risk assessment model of aircraft performance (Wang et al , 2019b), aircraft performance prediction model (Tong et al , 2018), flight dynamic model (Höhndorf et al , 2017), fuel consumption model of the climbing phase of departure aircraft (Huang et al , 2019), risk analysis model of flight operation (Shao et al , 2012; Wang et al , 2014) and aircraft performance mode (Hernández Herrera, 2018) are constructed to evaluate aircraft performance and operation risk, identify critical links of flight operation and high-risk operations, analyze pilot’s operation behavior, mitigate aircraft accidents and improve flight management efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…Risk assessment model of aircraft performance (Wang et al , 2019b), aircraft performance prediction model (Tong et al , 2018), flight dynamic model (Höhndorf et al , 2017), fuel consumption model of the climbing phase of departure aircraft (Huang et al , 2019), risk analysis model of flight operation (Shao et al , 2012; Wang et al , 2014) and aircraft performance mode (Hernández Herrera, 2018) are constructed to evaluate aircraft performance and operation risk, identify critical links of flight operation and high-risk operations, analyze pilot’s operation behavior, mitigate aircraft accidents and improve flight management efficiency.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment method for controlled flight into terrain of airlines based on QAR data

Guo

Sun

et al. 2023

AEAT

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Purpose The purpose of this paper is to assess the risk of controlled flight into terrain (CFIT) for airlines and to develop a practical method for evaluating and predicting CFIT risk to ensure safe and efficient airline operations. Design/methodology/approach In accordance with the monitoring project specification issued by the Flight Standards Department of the Civil Aviation Administration of China (CAAC), a preliminary draft of evaluation indicators for CFIT risk was developed based on the literature review and semi-structured interviews. Fifteen aviation experts were then selected and invited to participate in a Delphi method to revise the draft. Analytic hierarchy process (AHP) and entropy weight method were used to determine the combined weight of the indicators. The variable fuzzy set model and quick access recorder (QAR) data were applied to evaluate the CFIT risk of an airline from 2007 to 2018, and the classification results were compared with actual operational data. Findings The research findings reveal that the six most significant monitoring items affecting CFIT risk are incorrect configuration settings during landing, loss of altitude during climbing, ground proximity warning, G/S deviation, flap extension delay during landing and incorrect takeoff configuration. The CFIT risk of airlines has shown an increasing trend since 2015. The values in 2010, 2017 and 2018 were greater than 2 and less than 2.5, indicating that the CFIT risk is at Level 2, close to Level 3, and the risk is low but approaching medium. Practical implications Using the combination weight determined by AHP and entropy weight method to rank the weight of 15 monitoring items, airlines can take necessary measures (simulator training, knowledge training) to reduce the occurrence of monitoring items with high weight to reduce CFIT risk. This risk assessment method can quantitatively evaluate the CFIT risk of airlines and provide theoretical guidance and technical support for airlines to formulate safety management measures and flight training programs, enabling the interconnection between QAR data and flight quality. Originality/value The proposed method in this study differs from traditional approaches by offering a quantitative assessment of CFIT risk for airlines and enabling the interconnection between QAR data and flight quality.

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Reconstruction of Aircraft States During Landing Based on Quick Access Recorder Data

Cited by 12 publications

References 4 publications

Modeling of Stochastic Wind Based on Operational Flight Data Using Karhunen–Loève Expansion Method

Modeling of Stochastic Wind Based on Operational Flight Data Using Karhunen–Loève Expansion Method

Statistical Dependence Analyses of Operational Flight Data Used for Landing Reconstruction Enhancement

Risk assessment method for controlled flight into terrain of airlines based on QAR data

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