A Unified Statistical Framework for Multicopter Propeller Damage Diagnosis Based on Functionally Pooled Models and Bayesian Quantification: Experimental Flight Test Assessment
For multicopter operators and UAV maintenance, this framework offers a data-efficient, interpretable, and statistically rigorous solution for structural health monitoring, enabling damage detection, motor-level identification, and magnitude estimation from standard sensors.
This work introduces a stochastic time series framework for multicopter propeller damage diagnosis using only standard IMU data, achieving consistent cross-flight performance without case-specific retuning. The Bayesian quantification method provides more stable damage estimates with explicit uncertainty bounds compared to conventional batch-based approaches.
In this work, a stochastic time series-based framework is introduced for multicopter propeller damage diagnosis using functionally pooled autoregressive (FP-AR) models. The framework addresses damage detection, motor-level identification, and damage magnitude estimation using only standard inertial measurement unit data, without requiring additional sensors. Functional pooling provides a compact representation of system dynamics across varying operating conditions and supports reliable model estimation from short data records. Damage detection is performed through statistical testing of prediction residuals, damage identification through model selection, and damage quantification through a Bayesian inference scheme that provides posterior estimates and uncertainty bounds. The framework is experimentally evaluated through outdoor flight tests of a custom-built hexacopter following figure-eight trajectories under ambient wind disturbances. Six IMU channels, including three-axis acceleration and angular velocity, are analyzed across multiple motors and propeller damage levels. The results demonstrate consistent cross-flight performance without case-specific retuning. Compared with conventional batch-based quantification, the Bayesian approach provides more stable estimates and explicit uncertainty characterization. Overall, the proposed framework offers a data-efficient, interpretable, and statistically rigorous solution for multicopter structural health monitoring.