Applying Probabilistic Risk Assessment to Safety Risk Analysis in Aviation

Balakrishna, P.; Borener, Sherry; Crook, Ian; Durston, Alan; Robinson, Mindy J.

doi:10.4018/978-1-4666-9458-3.ch013

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…Similarly, the FAA uses data mining techniques to monitor operational parameters that define Key Performance Indicators (KPI) throughout the NAS to identify issues and modify operations before issues become hazards; 6 EUROCONTROL's Automatic Safety Monitoring Tool (ASMT) serves a comparable purpose in Europe. 7,8 Finally, an even broader, off-line, approach to finding accident and incident precursors uses the integrated FAA Integrated Safety Assessment Model (ISAM) 9 and EUROCONTROL Incident Risk Assessment Model 10 to link known precursors and hazards involved with every identified cause of accidents and incidents worldwide. The integrated model can be used for risk-based rule-making.…”

Section: Introductionmentioning

confidence: 99%

Initial Demonstration of the Real-Time Safety Monitoring Framework for the National Airspace System Using Flight Data

Roychoudhury

Daigle

Goebel

et al. 2016

16th AIAA Aviation Technology, Integration, and Operations Conference

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As new operational paradigms and additional aircraft are being introduced into the National Airspace System (NAS), maintaining safety in such a rapidly growing environment becomes more challenging. It is therefore desirable to have an automated framework to provide an overview of the current safety of the airspace at different levels of granularity, as well an understanding of how the state of the safety will evolve into the future given the anticipated flight plans, weather forecast, predicted health of assets in the airspace, and so on. Towards this end, as part of our earlier work, we formulated the Real-Time Safety Monitoring (RTSM) framework for monitoring and predicting the state of safety and to predict unsafe events. In our previous work, the RTSM framework was demonstrated in simulation on three different constructed scenarios. In this paper, we further develop the framework and demonstrate it on real flight data from multiple data sources. Specifically, the flight data is obtained through the Shadow Mode Assessment using Realistic Technologies for the National Airspace System (SMART-NAS) Testbed that serves as a central point of collection, integration, and access of information from these different data sources. By testing and evaluating using real-world scenarios, we may accelerate the acceptance of the RTSM framework towards deployment. In this paper we demonstrate the framework's capability to not only estimate the state of safety in the NAS, but predict the time and location of unsafe events such as a loss of separation between two aircraft, or an aircraft encountering convective weather. The experimental results highlight the capability of the approach, and the kind of information that can be provided to operators to improve their situational awareness in the context of safety.

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Section: Introductionmentioning

confidence: 99%