arXiv cs.AI论文
基于飞行日志的无人机螺旋桨健康监测的变质人工年龄评分决策支持原型
该论文提出一种基于飞行日志的无人机螺旋桨健康监测决策支持原型，利用变质人工年龄评分（AAS）方法。通过计算轨迹跟踪误差、姿态不稳定等六个健康指标，并采用变质充分性关系和冗余调整的AAS公式，对健康基线和三种缺陷螺旋桨案例进行回顾性评估。结果显示，不同严重程度的故障通过不同通道表现，支持多指标决策层用于飞行后维护优先级划分和自主系统监督。
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Abstract:Drone propeller faults can create safety and reliability risks when their effects are distributed across multiple flight-log channels rather than appearing as a single diagnostic signal. This paper proposes a Metamorphic Artificial Age Score (AAS) decision-support prototype for flight-log-based drone propeller health monitoring. Using selected historical real flight logs from the 2024 DronePropA public dataset, the framework computes six health-related indicators from raw MATLAB matrices: trajectory tracking error, attitude instability, thrust-command burden, motor-command imbalance, ESC-command instability, and battery-level stress. These indicators are normalized relative to a healthy baseline and evaluated through candidate scoring policies, metamorphic adequacy relations, and a redundancy-adjusted AAS formulation. In this context, AAS is used as a structural policy-adequacy and burden measure rather than as a chronological age measure. A controlled retrospective evaluation was performed using one healthy baseline and three defective propeller cases under the same speed profile and trajectory. The healthy case was assigned to routine monitoring. The Severity 1 case was dominated by ESC-command instability and assigned to maintenance review. The Severity 2 case reached maximum motor-command and ESC-command burden, while the Severity 3 case reached maximum trajectory tracking error; both triggered mandatory inspection. The results show that propeller fault effects may appear through different operational channels, supporting the need for a multi-indicator decision-support layer for post-flight maintenance prioritization and autonomous-system oversight.