A Metamorphic Artificial Age Score Decision-Support Prototype for Flight-Log-Based Drone Propeller Health Monitoring
Proposes a Metamorphic Artificial Age Score (AAS) decision-support prototype that detects drone propeller faults distributed across multiple flight-log channels rather than relying on single diagnostic signals Computes six health indicators from the 2024 DronePropA dataset: trajectory tracking error, attitude instability, thrust-command burden, motor-command imbalance, ESC-command instability, and battery-level stress Uses normalization against a healthy baseline, metamorphic adequacy relations,
Analysis
TL;DR
- Proposes a Metamorphic Artificial Age Score (AAS) decision-support prototype that detects drone propeller faults distributed across multiple flight-log channels rather than relying on single diagnostic signals
- Computes six health indicators from the 2024 DronePropA dataset: trajectory tracking error, attitude instability, thrust-command burden, motor-command imbalance, ESC-command instability, and battery-level stress
- Uses normalization against a healthy baseline, metamorphic adequacy relations, and a redundancy-adjusted AAS formulation to classify severity levels
- Retrospective evaluation with one healthy case and three defective propeller cases demonstrated clear severity分级: Severity 1 (ESC instability → maintenance review), Severity 2 (motor/ESC burden → mandatory inspection), Severity 3 (trajectory error → mandatory inspection)
- Demonstrates that fault signatures vary across operational channels, validating the need for multi-indicator decision-support in post-flight maintenance prioritization
Why It Matters
This work addresses a critical gap in autonomous drone operations: propeller faults manifest differently depending on failure mode, making single-signal diagnostics insufficient for reliable health monitoring. The AAS framework offers a practical, data-driven approach for maintenance prioritization that could be integrated into autonomous system oversight pipelines, reducing safety risks and operational downtime in drone fleets.
Technical Details
- Framework: Metamorphic Artificial Age Score (AAS) — a structural policy-adequacy and burden measure, not a chronological age metric, using metamorphic relations to validate output consistency across input transformations
- Data Source: Historical real flight logs from the 2024 DronePropA public dataset, processed from raw MATLAB matrices
- Six Health Indicators: (1) Trajectory tracking error, (2) Attitude instability, (3) Thrust-command burden, (4) Motor-command imbalance, (5) ESC-command instability, (6) Battery-level stress — all normalized relative to a healthy baseline
- Evaluation Methodology: Controlled retrospective comparison of one healthy baseline and three defective propeller cases under identical speed profiles and trajectories, with candidate scoring policies and redundancy-adjusted AAS formulation
- Classification Outcomes: Healthy → routine monitoring; Severity 1 (ESC-dominated) → maintenance review; Severity 2 (motor+ESC burden) and Severity 3 (trajectory error) → mandatory inspection
Industry Insight
- The finding that fault effects distribute across different operational channels (ESC, motor, trajectory) rather than concentrating in one signal suggests that single-threshold monitoring systems will miss significant failure modes; multi-indicator frameworks should become standard in drone fleet management
- The AAS approach's reliance on post-flight log analysis rather than real-time sensing makes it deployable on existing drone hardware without additional sensors, offering a low-cost path to improved safety compliance
- As autonomous drone operations scale in logistics and inspection sectors, decision-support prototypes like this that bridge raw telemetry and maintenance actionability will become essential infrastructure for regulatory compliance and operational reliability
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