发表机构
Institute for Sustainable Industries and Liveable Cities (ISILC); Victoria University（可持续产业与宜居城市研究所（ISILC）; 维多利亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对无人机螺旋桨故障影响分散多通道的问题，提出变形人工年龄评分（AAS）决策支持原型，基于2024 DronePropA数据集计算6项健康指标，评估缺陷案例并验证了多指标决策层的必要性。
AI 中文摘要
无人机螺旋桨故障若其影响分布在多个飞行日志通道而非表现为单一诊断信号，会带来安全与可靠性风险。本文提出一种用于基于飞行日志的无人机螺旋桨健康监测的变形人工年龄评分（AAS）决策支持原型。该框架使用2024 DronePropA公共数据集中选定的历史真实飞行日志，从原始MATLAB矩阵中计算6项健康相关指标：轨迹跟踪误差、姿态不稳定性、推力指令负担、电机指令失衡、ESC指令不稳定性以及电池电量应力。这些指标相对于健康基线进行归一化，并通过候选评分策略、变形充分性关系以及经冗余调整的AAS公式进行评估。在此背景下，AAS被用作结构策略充分性与负担度量，而非时间年龄度量。在相同速度剖面与轨迹下，使用1个健康基线案例和3个缺陷螺旋桨案例进行了受控回顾性评估：健康案例被分配至常规监测；1级严重度案例以ESC指令不稳定性为主，被分配至维护审查；2级严重度案例达到电机指令与ESC指令负担的最大值，3级严重度案例达到轨迹跟踪误差的最大值，两者均触发强制检查。结果表明，螺旋桨故障影响可能通过不同的操作通道显现，支持构建多指标决策支持层，用于飞行后维护优先级排序与自主系统监督。
英文摘要
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.
Comments21 pages, 7 tables. This paper presents a retrospective decision-support prototype using selected flight logs from the public DronePropA dataset. The framework integrates flight-log-derived feature extraction, metamorphic adequacy testing, and a redundancy-adjusted Artificial Age Score formulation for drone propeller health monitoring
