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Application of temporal logic for safety supervisory control and model-based hazard monitoring

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  • Favarò, Francesca M.
  • Saleh, Joseph H.

Abstract

In this work, we extend a previously introduced framework for safety supervisory control with the ingredient of Temporal Logic (TL) to improve both accident prevention and dynamic risk assessment. We examine the synergies obtained from integrating model-based hazard modeling/monitoring with the verification of safety properties expressed in TL. This expanded framework leverages tools and ideas from Control Theory and Computer Science, and is meant to guide safety intervention both on-line and off-line, either during the design stages or during operation to support operator's situational awareness and decision-making in the face of emerging hazardous situations. We illustrate these capabilities and the insight that results from the integration of the proposed ingredients through a detailed case study. The study involves a runway overrun by a business jet, and it shows how hardware, software, and operators’ control actions and responses can be integrated within the proposed framework. The aircraft suffered from a faulty logic in the Full Authority Digital Engine Computer (FADEC), which prevented the pilot from activating the thrust reversers in a particular operational scenario. We examine the accident sequence against three system safety principles expressed in TL: the fail-safe principle, the defense-in-depth principle, and the observability-in-depth principle. The framework is implemented in Simulink and Stateflow, and is shown to provide important feedback for dynamic risk assessment and accident prevention. When applied on-line, it provides warning signs to support the sensemaking of emerging hazardous situations, and identifying adverse conditions that are closer to being released. When applied off-line, it provides diagnostic information regarding missing or inadequate safety features embedded in the system. For the specific case study, we propose a new TL safety constraint (based on speed measurements and the history of pressure sensors from the landing gears) to be incorporated in this and other aircraft FADEC, and that could have prevented the hazardous situation, in this case a rejected takeoff following tire explosion, from turning into a deadly accident. We conclude with some recommendations to prevent similar accident recurrences and to improve accident prevention.

Suggested Citation

  • Favarò, Francesca M. & Saleh, Joseph H., 2018. "Application of temporal logic for safety supervisory control and model-based hazard monitoring," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 166-178.
  • Handle: RePEc:eee:reensy:v:169:y:2018:i:c:p:166-178
    DOI: 10.1016/j.ress.2017.08.012
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    References listed on IDEAS

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    1. Foreman, Veronica L. & Favaró, Francesca M. & Saleh, Joseph H. & Johnson, Christopher W., 2015. "Software in military aviation and drone mishaps: Analysis and recommendations for the investigation process," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 101-111.
    2. Favarò, Francesca M. & Jackson, David W. & Saleh, Joseph H. & Mavris, Dimitri N., 2013. "Software contributions to aircraft adverse events: Case studies and analyses of recurrent accident patterns and failure mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 131-142.
    3. Favarò, Francesca M. & Saleh, Joseph H., 2016. "Toward risk assessment 2.0: Safety supervisory control and model-based hazard monitoring for risk-informed safety interventions," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 316-330.
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    Cited by:

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    2. Tariq Mumtaz & Shahabuddin Muhammad & Muhammad Imran Aslam & Irfan Ahmed, 2022. "Inter-slice resource management for 5G radio access network using markov decision process," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 79(4), pages 541-557, April.
    3. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Elena Zaitseva & Vitaly Levashenko & Ravil Mukhamediev & Nicolae Brinzei & Andriy Kovalenko & Adilkhan Symagulov, 2023. "Review of Reliability Assessment Methods of Drone Swarm (Fleet) and a New Importance Evaluation Based Method of Drone Swarm Structure Analysis," Mathematics, MDPI, vol. 11(11), pages 1-26, June.
    5. Pantelaki, Evangelia & Papatheodorou, Andreas, 2022. "Behind the scenes of glamour: A systematic literature review of the business aviation sector," Journal of Air Transport Management, Elsevier, vol. 105(C).

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