Data-Driven Probabilistic Health Monitoring on a Hexacopter via Time-Series Assisted Machine Learning Methods

This study presents a statistical approach for detecting and estimating damage to multicopter propellers through a comprehensive probabilistic model. The methodology is derived from model-based analysis and applied within the time series statistical techniques. This research accounts for uncertainties in the estimation process and offers confidence intervals for assessing the extent of damage to the propellers. The framework employs functionally pooled (FP) models characterized by parameters that depend on damage sizes, proper statistical estimation, and decision-making schemes. The validation and assessment are assessed via a hexacopter flying in circles with a constant velocity and altitude under turbulence. The damage size ranges from healthy to 10 mm. The method achieves fast damage detection and precise magnitude estimation based on a segment of a single measured signal obtained from aircraft sensors during flight.