Risk Topology Construction Method of Flight Manipulation in Turbulent-Windshear Conditions Based on Multivariate Copula Theory

Wang, Guozhi; Xu, Haojun; Pei, Binbin; Xue, Yuan; Duan, Xiaocong

doi:10.1109/access.2020.3008060

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…Kuo and Lu [103] used Fuzzy MADM approach to assess risk of a metropolitan construction project. Risk analysis can be done using many methods such as Monte Carlo Simulation [104], [105], [106], [107], and [108], Event Trees [109] and [110], Fault Trees [111], [112] and [25], Dynamic Event trees [113], Markov models [114], Failure mode and Effective Analysis [115], [93], and [116], and Petri net [117], [118], [119] and [120]. Due to the importance of these products and the critical related risk, some researches are performed in order to evaluate risks and identify best trajectory to travel as in [121], [122], and [123].…”

Section: Literature Surveymentioning

confidence: 99%

Dynamic Decision Making Process for Dangerous Good Transportation Using a Combination of TOPSIS and AHP Methods With Fuzzy Sets

Kanj,

Kotb,

Alakkoumi

et al. 2024

IEEE Access

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Dangerous Goods Transportation in smart cities is one of the most intriguing topics that researchers are interested in to reduce the risks caused by hazardous events. This article proposes a novel technique to avoid hazardous events during the transportation of dangerous goods using realtime information provided by the cloud. This article applies Fuzzy AHP-TOPSIS hybrid method for risk analysis applied to Dangerous goods transportation in a smart city. The article assumes that we have three criteria that dictate the choice of routs the vehicle follows: cost, duration, and risk. The main objective is to choose the best route through calculated weights assigned to the three criteria based on predefined pairwise comparisons. The study is done on both static and dynamic environments. In static environments, the decision is made and is followed prior to actions and no change of actions is made even if criteria values change. In dynamic environments on the other hand, the decision could change during the transportation process due to the change of the valuation the criteria contribute to. The study shows that risk is reduced and therefore safety is increased for the transportation of dangerous goods.

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Section: Literature Surveymentioning

confidence: 99%

Dynamic Decision Making Process for Dangerous Good Transportation Using a Combination of TOPSIS and AHP Methods With Fuzzy Sets

Kanj,

Kotb,

Alakkoumi

et al. 2024

IEEE Access

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“…Pei et al [21] adopted an extremum theory to quantify flight risk under icing conditions. Subsequently, Wang et al [22] adopted a multivariate copula model to evaluate landing risk under turbulent-windshear conditions. Moreover, deep neural networks were developed for supporting risk assessment and fault diagnosis [23,24].…”

Section: Introductionmentioning

confidence: 99%

Risk Quantification and Visualization Method for Loss-of-Control Scenarios in Flight

Wang

Pei

et al. 2023

Aerospace

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This paper proposes a flight risk analysis method that combines risk assessment and visual deduction to study the causes of flight accidents, specifically the loss of control caused by failure factors. The goal is to explore the impact of these failure factors on loss-of-control events and illustrate the risk evolution under different scenarios in a clear and intuitive manner. To achieve this, the paper develops a failure scenario tree to guide flight simulations under different loss-of-control scenarios. The next step involves developing a multi-parameters risk assessment method that can quantify flight risk at each time step of the flight simulation. This assessment method uses entropy weight and a grey correlation algorithm to assign variable weights to the different parameters. Finally, the paper presents the visual deduction of the risk evolution process under different loss-of-control scenarios using a risk tree that concisely represents the time-series risk assessment results and failure logical chains. Taking three common failure factors (actuator failure, engine failure, and wing icing) as cases, the paper designs 25 different loss-of-control scenarios to demonstrate the flight risk analysis method. By comparing the risk evolution process under different loss-of-control scenarios, the paper explores the impact of the failure factors on flight safety. The analysis results indicate that this method combines risk analysis from both individual and global perspectives, enabling effective analysis of risk evolution in loss-of-control events.

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Conceptual design and preliminary experiment of icing risk management and protection system

Pei

et al. 2022

Chinese Journal of Aeronautics

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Risk Topology Construction Method of Flight Manipulation in Turbulent-Windshear Conditions Based on Multivariate Copula Theory

Abstract: The statements made herein are solely the responsibility of the authors.

Cited by 5 publications

References 26 publications

Dynamic Decision Making Process for Dangerous Good Transportation Using a Combination of TOPSIS and AHP Methods With Fuzzy Sets

Dynamic Decision Making Process for Dangerous Good Transportation Using a Combination of TOPSIS and AHP Methods With Fuzzy Sets

Risk Quantification and Visualization Method for Loss-of-Control Scenarios in Flight

Conceptual design and preliminary experiment of icing risk management and protection system

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