Toward More Reliable UAVs: Interpretable Machine Learning for Understanding Propeller Fault Separability

Authors

Saadi Turied Kurdi, College of Engineering, Al-Bayan University, Baghdad, IraqFollow
Luttfi A. Al-Haddad, College of Mechanical Engineering, University of Technology- Iraq, Baghdad 10066, IraqFollow
Latif Ibraheem, Mechanical Engineering Department, Memorial University of Newfoundland, Newfoundland and Labrador, CanadaFollow

Abstract

This study investigates the fundamental limits of UAV propeller fault discrimination using an interpretable XGBoost–SHAP framework applied to the high-resolution DronePropA dataset. Three fault types—edge-cut, structural crack, and surface erosion—were evaluated across three severity levels under constrained flight conditions to isolate the physical signatures of degradation. A curated subset of 1 kHz IMU and ESC telemetry from Drone 1 (trajectory t1, speed SP1) enabled analysis of fault separability independent of kinematic variability. Despite deploying a moderately complex XGBoost classifier (300 estimators, depth = 6) and a comprehensive multi-domain feature set, the model achieved a performance ceiling of 38.04% accuracy and 38.27% macro-F1 which reflects the inherent aerodynamic similarity among fault classes rather than model limitations. Confusion analysis revealed structured misclassification, with 62% of total errors arising from overlap between edge-cut and surface-erosion states, while crack-major exhibited markedly higher recall (66.7%) due to its distinctive high-frequency dynamics. SHAP analysis further demonstrated that only a subset of features—most notably wx_Hs (0.082) and omega_fpk_mean3 (0.058)—dominate fault separability. These findings mean that the proposed physics-informed diagnostic framework indicates that certain propeller faults may be intrinsically indistinguishable using minimal onboard sensors alone.

Recommended Citation

Kurdi, Saadi Turied; Al-Haddad, Luttfi A.; and Ibraheem, Latif (2026) "Toward More Reliable UAVs: Interpretable Machine Learning for Understanding Propeller Fault Separability," Terra Joule Journal: Vol. 2: Iss. 1, Article 7.
DOI: https://doi.org/10.64071/3080-5724.1028