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<p>The propulsion systems of a multi-rotor unmanned aerial vehicle (UAV) is crucial, as it directly affects the UAV’s performance, efficiency, and safety. Since the components of the UAV propulsion system are highly interconnectioned, we developed a fuzzy fault tree analysis method to analysis the varying reliability under different fault conditions. Combining the fuzzy fault tree analysis of the T-S model and the UAV propulsion system model, we constructed a fuzzy fault tree of the T-S type for the system and performed a reliability analysis. This fuzzy fault tree allows us to model the system from two perspectives: fuzzy failure rate and failure degree. Consequently, two methods can be used for failure analysis of UAV systems. The first method involves calculating the system’s fuzzy failure rate based on the component’s fuzzy failure rate. The second method calculates the fuzzy failure rate of the system based on the failure degree of the component. The computational results indicate that both methods are well-suited for fault diagnosis in UAV propulsion systems. Compared to traditional fault tree analysis, which does not subdivide fault degrees, the proposed methods provide more accurate fault rate assessments.</p>

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Fault diagnosis of multi-rotor unmanned aerial vehicle propulsion systems based on T-S fault tree

  • 期刊论文

  • 发表日期：

    2025年4月25日

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  • Lining Tan,
  • Fei Xue,
  • Guodong Jin,
  • Kai Shen

摘要

The propulsion systems of a multi-rotor unmanned aerial vehicle (UAV) is crucial, as it directly affects the UAV’s performance, efficiency, and safety. Since the components of the UAV propulsion system are highly interconnectioned, we developed a fuzzy fault tree analysis method to analysis the varying reliability under different fault conditions. Combining the fuzzy fault tree analysis of the T-S model and the UAV propulsion system model, we constructed a fuzzy fault tree of the T-S type for the system and performed a reliability analysis. This fuzzy fault tree allows us to model the system from two perspectives: fuzzy failure rate and failure degree. Consequently, two methods can be used for failure analysis of UAV systems. The first method involves calculating the system’s fuzzy failure rate based on the component’s fuzzy failure rate. The second method calculates the fuzzy failure rate of the system based on the failure degree of the component. The computational results indicate that both methods are well-suited for fault diagnosis in UAV propulsion systems. Compared to traditional fault tree analysis, which does not subdivide fault degrees, the proposed methods provide more accurate fault rate assessments.