An Analysis of Hard Landing Incidents Based on Flight QAR Data

Wang, Lei; Wu, Changxu; Sun, Ranhao; Zhen-xin, Cui

doi:10.1007/978-3-319-07515-0_40

Cited by 18 publications

(6 citation statements)

References 7 publications

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“…When the analysis variables were examined for normality, several of the variables were found to be only roughly normally distributed, which led to the decision to use the more robust non-parametric Mann-Whitney U test instead of the more common parametric T-test (which was used by Wang et al, 2018Wang et al, , 2014. The Mann-Whitney U test is based on rank sums of two different distributions (Johnson, Miller, & Freund, 2018).…”

Section: Discussionmentioning

confidence: 99%

Development of a Metric Concept that Differentiates Between Normal and Abnormal Operational Aviation Data

Stogsdill

Belvisi²,

Ulfvengren

2021

Risk Analysis

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There is a strong and growing interest in using the large amount of high‐quality operational data available within an airline. One reason for this is the push by regulators to use data to demonstrate safety performance by monitoring the outputs of Safety Performance Indicators relative to targeted goals. However, the current exceedance‐based approaches alone do not provide sufficient operational risk information to support managers and operators making proximate real‐time data‐driven decisions. The purpose of this study was to develop and test a set of metrics which can complement the current exceedance‐based methods. The approach was to develop two construct variables that were designed with the aim to: (1) create an aggregate construct variable that can differentiate between normal and abnormal landings (row_mean); and (2) determine if temporal sequence patterns can be detected within the data set that can differentiate between the two landing groups (row_sequence). To assess the differentiation ability of the aggregate constructs, a set of both statistical and visual tests were run in order to detect quantitative and qualitative differences between the data series representing two landing groups prior to touchdown. The result, verified with a time series k‐means cluster analysis, show that the composite constructs seem to differentiate normal and abnormal landings by capturing time‐varying importance of individual variables in the final 300 seconds before touchdown. Together the approaches discussed in this article present an interesting and complementary way forward that should be further pursued.

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Section: Discussionmentioning

confidence: 99%

Development of a Metric Concept that Differentiates Between Normal and Abnormal Operational Aviation Data

Stogsdill

Belvisi²,

Ulfvengren

2021

Risk Analysis

View full text Add to dashboard Cite

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“…In the realm of a quantitative assessment of hard landing risk, Wang Lei et al [20] applied statistical modeling to establish a quantitative assessment model for heavy landing risk, evaluating the likelihood and severity of occurrences based on parameter distribution functions and algorithms and calculating risk levels. They constructed a landing overrun analysis model based on QAR data, utilizing Bayesian network [21] analysis and employing GTT algorithms for parameter learning to establish a landing overrun risk Bayesian network model. Wang Le et al [22] divided 128 cases of QAR data into two groups based on the identification thresholds for normal landings and long landings, conducted inter-group comparisons of flight parameters, established logistic regression and linear regression models, and analyzed the potential flight operations that lead to performance differences in long landing events, ultimately identifying key operations that affect landing distance during flare.…”

Section: Other Flight Safety Studiesmentioning

confidence: 99%

Impacts of Flight Operations on the Risk of Runway Excursions

Wang,

Yuan,

Liu

et al. 2024

Applied Sciences

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The Quick Access Recorder (QAR), as an onboard device used for monitoring and recording flight parameters, has been extensively installed on various types of aircraft. Recently, there has been a significant focus on studying the typical flight safety event of runway excursions based on QAR data. However, there is limited research that combines the analysis of runway excursion risks with flight operations, and there is also a scarcity of studies that divide the investigation of the landing phase into multiple key stages. In this paper, we propose a comparative analysis of operational characteristics and risks associated with runway excursions from the perspective of operational styles. A total of 2087 flights were classified on the basis of touchdown distance, taxiing distance, and magnetic heading changes and were divided into three styles based on these indicators. Subsequently, we analyze flight operations and attitudes at five key stages: runway threshold, flare, speed brake deployment, touchdown, and reverse thrust activation. Furthermore, we employ the selection criteria of pilot proficiency levels to filter out standard operational curves. The curve similarity is used to compare the difference between the actual operating curves and the standard curves. Finally, we employ typical correlation analysis to explore the relationship between touchdown distance and operational variances. The findings indicate that Style 1 pilots exhibit the lowest probability of runway excursions, yet their maneuvers potentially elevate the risk of hard landing events.

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“…The research on evaluating based on sensing data has attracted much attention worldwide. Researchers conducted numerous studies on aviation safety [2][3][4], flight quality [5], and flight monitoring [6][7][8] with the help of sensor flight parameter data, and provided a large amount of data in support of flight operation quality assessment [9][10][11]. From a psychological perspective, the evolution of human-based behavioral research was accompanied by the development of human-monitoring devices.…”

Section: Introductionmentioning

confidence: 99%

Behavioral Indicator-Based Initial Flight Training Competency Assessment Model

Yang

Zhang

2023

Applied Sciences

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Ensuring training safety is paramount to flight schools. In response to the inadequacy of traditional flight training assessment for comprehensive quantitative evaluation of cadet competency, an initial flight training competency assessment standard based on behavioral indicators was developed and optimized using the VENN model. Firstly, the Assessor Score Measurement Form (ASMF) was constructed according to the requirements of the Training Evaluation Worksheet specification, such as typical subjects, observations, and completion criteria. Secondly, based on the basic principles of the experience of the flight expert and the Competency-Based Training and Assessment (CBTA), a matrix of correlations between the observations and each competency-based behavioral indicator was created to construct a competency assessment matrix. In addition, a two-dimensional model for representing competency items characterized by behavioral indicators was established and an optimization model for competency assessment criteria was constructed. Finally, through combining actual flight training data, the proposed method was validated in the flight screening check phase. The results show that the optimized flight training competency assessment scheme can be well quantified and matched to real instructor ratings with an accuracy of 84%. The assessment worksheet, the assessment matrix, and the VENN competency rating model can be adapted to the different teaching requirements of each flight phase, achieving a perfect match between the behavioral indicators and the competency items, which is highly versatile. The proposed model can more accurately reflect the core competencies of flight trainees, enable quantitative assessment of behavioral indicators and competency items, and provide support for subsequent training of trainees.

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An Analysis of Hard Landing Incidents Based on Flight QAR Data

Cited by 18 publications

References 7 publications

Development of a Metric Concept that Differentiates Between Normal and Abnormal Operational Aviation Data

Development of a Metric Concept that Differentiates Between Normal and Abnormal Operational Aviation Data

Impacts of Flight Operations on the Risk of Runway Excursions

Behavioral Indicator-Based Initial Flight Training Competency Assessment Model

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