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Dynamic Risk Assessment of Unmanned Aerial Vehicles (UAVs)

  • Author / Creator
    Chowdhury, Aungshula
  • Unmanned Aerial Vehicle (UAV) systems are increasingly being utilized in public airspaces, necessitating a high level of reliability to minimize risks to the general public. This research focuses on optimizing the probability of mission success by effectively analyzing and managing risks during various operational phases of UAV missions. Given the dearth of established reliability models for UAVs, a systems reliability modeling methodology based on the Structural Analysis and Design Technique (SADT) is employed, along with a conditional risk analysis approach for each mission activity. Through the application of Hazard and Operability (HAZOP) techniques and Failure Modes and Effects Analysis (FMEA), the risks associated with specific mission activities are systematically identified, while incorporating stopping conditions to ensure risks are maintained at an acceptable level. Moreover, the impacts and uncertainties of internal and external failure causes for each activity are described, ranked, and addressed according to their risk priorities.

    This research introduces a dynamic risk assessment framework that optimizes mission success probability through comprehensive risk analysis and management across diverse operational phases. Furthermore, this multifaceted risk mitigation approach is applied to enhance the reliability of rotary and fixed-wing UAVs in an industrial setting at a Canadian space data company. The tailored model specifically targets applications such as agricultural farm imagery collection and methane leak detection in pipelines. Identified risks throughout different operational phases are meticulously mitigated through well-defined controls. To ensure the effectiveness of the control measures, a rigorous analysis known as "Minimum Bayes Risk" is employed. This analysis enables the selection of the optimal mitigation strategy from a range of available options by estimating mission reliability through the calculation of posterior probabilities of failure states. By prioritizing failure modes and expertly selecting the most effective controls for each risk, the proposed strategy is further validated by subject matter experts (SMEs) from the industry. This expert validation instills confidence in the effectiveness of the chosen control strategies, which successfully reduce risk and enhance the probability of mission success. Additionally, a comprehensive checklist is provided to drone operators, outlining the identified risks and their corresponding mitigation strategies.

    The outcome of this research is a comprehensive and robust approach that effectively reduces risks and enhances the likelihood of mission success. The application of the "Minimum Bayes Risk" analysis, combined with expert validation, ensures the selection of control strategies that efficiently mitigate risks associated with UAV operations. This approach contributes to the advancement of UAV reliability, particularly in the context of agricultural farm imagery collection and methane leak detection in pipelines. The adaptability and tailored nature of the proposed model facilitates the efficient management of risks across different operational phases, ultimately reducing the potential for failures and enhancing mission success probability. As UAV usage continues to expand, this multifaceted risk mitigation approach holds significant promise for ensuring the reliability and safety of UAV systems in various real-world scenarios.

  • Subjects / Keywords
  • Graduation date
    Fall 2023
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/r3-nb6z-3w97
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.