Research on Fault Diagnosis Method for Tethered Fire Extinguishing Drones Improved by TextCNN and FocalLOSS

In response to the problems of low efficiency, long time consumption, and unstable detection in current manual fault detection methods for unmanned aerial vehicles, this paper takes tethered firefighting unmanned aerial vehicles as the research object, applies neural networks to fault detection, and proposes and implements an improved unmanned aerial vehicle fault detection method based on TextCNN and FocalLoss. Firstly, based on the principle of threshold comparison and expert experience, define the types of drone faults, determine the characteristics of drone faults according to the fault types, and classify the faults accordingly; Secondly, design and implement a drone log analysis system to preliminarily organize flight logs, visually analyze flight control parameters, retrieve fault data, and construct a fault input dataset; Then, a drone fault detection method based on TextCNN and FocalLoss improvement was proposed to address the characteristics of text data and the problem of imbalanced dataset categories; Finally, experiments were conducted on a real fault flight dataset, demonstrating the effectiveness of the fault detection method.