The purpose of this paper is to propose a novel approach to enhancing the reliability of quadcopters, a class of unmanned aerial vehicles, through the development of a modeling technique known as Reliability Laws Stochastic Petri Nets (RLSPN). The RLSPN framework is designed to identify critical failure modes, analyze system vulnerabilities and support risk mitigation strategies. By doing so, the method aims to improve overall performance, extend operational lifespan and strengthen the safety and resilience of quadcopter systems.
The RLSPN framework integrates reliability laws within the SPN model, enabling systematic identification and evaluation of failure modes and their impact on system reliability. By exploiting structural similarities between RLSPN and oriented graphs, the approach facilitates graphical analysis using linear logic to trace root causes of failures. It further generates minimal feared states through the computation of Minimal Path Sets and Shortest Path Lengths, providing a robust mechanism for assessing both component level reliability and overall system performance.
This study demonstrates that the structural similarities between the RLSPN model and oriented graphs significantly enhance reliability analysis by enabling the identification of primary failure causes and inter-event dependencies. This enriched modeling capability supports the development of targeted improvement strategies for fault-prone components. Moreover, the integration of reliability laws provides deeper insights into component interrelationships, allowing for the identification of critical areas for intervention and highlighting the most and least reliable components for focused failure mitigation strategies.
This original research contributes to unmanned aerial vehicle reliability analysis by introducing the RLSPN model, which effectively combines deterministic and stochastic modeling techniques. By leveraging the strengths of both SPN and reliability theory, this comprehensive methodology enhances the reliability analysis of drone systems, facilitating the identification of key causes of critical failures through linear logic, Minimal Path Sets and Shortest Path Lengths.
