Adoption of a Bayesian Belief Network for the System Safety Assessment of Remotely Piloted Aircraft Systems

Washington, Achim; Clothier, Reece; Neogi, Natasha A.; Silva, Fernando; Hayhurst, Kelly J.; Williams, Brendan

doi:10.1016/j.ssci.2019.04.040

Cited by 18 publications

(12 citation statements)

References 24 publications

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“…can also be included. Graphical models such as BN have been used for applications including system reliability modelling Marquez et al (2010), safety analysis for remotely piloted aircraft Washington et al (2019) and automated ships Hanninen and Kujala (2012), and in the management of risk more generally Fenton and Neil (2007). With the BN modelling technique it is possible to describe any acyclic causal relationships between variables using graphical networks comprising nodes and directed edges between them.…”

Section: Modelling Epistemic Uncertaintiesmentioning

confidence: 99%

“…Moreover, recent studies in the field of safety and reliability show a significant increase in the use of BNs for risk assessment, taking advantage of their system modelling capabilities and inference techniques Washington et al (2019). Additionally, PGM models also allow the combination of expert knowledge and available data for likelihood estimation of desired variables during safety analysis.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge-Based Approach for System Level Electromagnetic Safety Analysis

Devaraj¹,

Ruddle²,

Khan³

et al. 2021

Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)

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Road vehicles and similarly complex systems are constructed by integrating many subsystems and components that are sourced from a large number of suppliers. This process may lead to the emergence of possible system-level safety issues, some of which could be caused by external or internal electromagnetic interference. Assurance of safety by demonstrating compliance with standard tests is becoming increasingly challenging as system complexity rises. This is due to the costs and practical limitations of both component and system-level electromagnetic compatibility testing. Hence, there is a need for additional methods to help estimate the likelihood of electromagnetic interference risks associated with such systems. Probabilistic graphical models, such as Bayesian and Markov networks, are able to provide a better visualization of various features and their relationships in a single graphical structure. Moreover, using template models, a general-purpose representation for various integrated components of a system can be developed for collective inference. Using such methods, this paper proposes a knowledge-based approach to assist risk management in system-level electromagnetic engineering. The purpose of using a knowledge-based approach is to be able to undertake safety risk analyses during the early stages of design, when many factors (e.g. internal, and external electromagnetic interference levels, physical location of the component) remain uncertain.

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Section: Modelling Epistemic Uncertaintiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Knowledge-Based Approach for System Level Electromagnetic Safety Analysis

Devaraj¹,

Ruddle²,

Khan³

et al. 2021

Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)

View full text Add to dashboard Cite

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“…All of the failure determination have been compared, from Federal Aviation Authority (FAA), European Union Aviation Safety Authority (EASA) and North Atlantic Treaty Organization (NATO) regulatory bodies. In the conclusion of the study, by Bayesian Belief Network failure condition severity classification is handled and it is pointed out that risk-based approach supports RPAS to be more systematic and objective particularly for emerging aviation systems [1].…”

Section: Introductionmentioning

confidence: 99%

Havacılıkta Parça Tasarımında Kök Neden Analiz Yöntemleri

Çelik¹

2020

Journal of Aviation

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Fault modes, which aren't detected throughout the design phase if they are not defined with precautions have the crucial potential on the quality of mature products. At first glance, the impression of the quality failures may be underestimated, before the design of aviation parts are implemented to production. But these failures may cause some problems in aviation safety and reliability. Therefore, the failures during the project phase should be investigated by root cause analysis and defining the precautions both increase effectiveness of design and quality of the outputs. The aim of this study is, emphasizing the importance of root cause analysis on the design of aviation parts, and stating the analysis methods, which have been used mainly. In the scope of this effort, aviation regulations are researched which encourages the root cause analysis accordingly, related methods are evaluated for the resolutions. The common methods are classified based on the hazard review of the failures and explained to point out for the researchers.

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“…Several previous studies investigated safety risk analysis for sUAS operations (Öztekin and Luxhøj 2008;Luxhøj and Öztekin 2009;Luxhøj 2013Luxhøj , 2015Wackwitz and Boedecker 2015;Ancel et al 2017;Barr et al 2017;Belcastro et al 2017;Krishnakumar et al 2017;Washington et al 2017b;Breunig et al 2018;Denney et al 2018;Lundby et al 2019). Wackwitz and Boedecker (2015) outlined and demonstrated best practices for performing a hazard analysis for sUAS.…”

Section: Introductionmentioning

confidence: 99%

“…Ancel et al (2017) used a BBN model to make real-time estimations of risk during an sUAS operation. A number of other studies have used BBNs for quantifying risk to sUAS (Öztekin and Luxhøj 2008;Luxhøj and Öztekin 2009;Luxhøj 2015;Washington et al 2017b;Denney et al 2018;Washington et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Weather Hazard Risk Quantification for sUAS Safety Risk Management

Roseman

Argrow

2020

Journal of Atmospheric and Oceanic Technology

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As the number of applications for small unmanned (i.e., remotely operated) aircraft systems (sUAS) continues to grow, comprehensive safety risk assessment studies are required to ensure their safe integration into the National Airspace System. One source of hazards for sUAS that has not been extensively addressed is adverse weather. A framework is presented for analyzing weather forecast data to provide sUAS operators with risk assessment information that they can use for making risk-aware decisions. The sUAS Weather Risk Model (sWRM) framework quantifies weather hazard risk for sUAS operations in rural to urban environments using weather forecast, population density, structure density, and sUAS data. sWRM is developed by following the safety risk management guidelines from the U.S. Federal Aviation Administration. Development of sWRM highlights a number of aerospace and meteorological research areas that must be addressed prior to weather risk models for sUAS becoming operational. Primary among these research areas is developing widely available finescale (<1 km) weather forecasts and conducting extensive sUAS flight-report studies to accurately estimate parameters of Bayesian belief network conditional probability tables used in the proposed framework. As a proof of concept, sWRM was applied over Boulder, Colorado, using the High-Resolution Rapid Refresh weather product. This initial demonstration of sWRM highlights the potential effectiveness of a detailed risk assessment model that takes into account high-resolution weather and environmental data.

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Adoption of a Bayesian Belief Network for the System Safety Assessment of Remotely Piloted Aircraft Systems

Cited by 18 publications

References 24 publications

Knowledge-Based Approach for System Level Electromagnetic Safety Analysis

Knowledge-Based Approach for System Level Electromagnetic Safety Analysis

Havacılıkta Parça Tasarımında Kök Neden Analiz Yöntemleri

Weather Hazard Risk Quantification for sUAS Safety Risk Management

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