A Fault Diagnosis Method for Avionics Equipment Based on SMOTEWB‐LGBM

Abstract

To tackle the common issue of imbalanced data classes in the fault diagnosis of avionics equipment, this study proposes a method that integrates the Synthetic Minority Oversampling Technique (SMOTE) with Boosting (SMOTEWB) and the Light Gradient Boosting Machine (LGBM). Initially, SMOTEWB is refined through a “successive one‐vs‐many balancing strategy,” which effectively addresses multiclass sample balance issues. The enhanced data is then employed for pattern recognition and classification using LGBM. Additionally, this study utilizes the Tree‐structured Parzen Estimator (TPE) method and five‐fold cross‐validation to optimize the model’s hyperparameters, thus improving diagnostic accuracy. Experimental validation using University of California Irvine (UCI) public datasets and real‐world avionics equipment fault data shows that the proposed SMOTEWB‐LGBM method outperforms other common methods in handling multiclass imbalanced datasets. This new approach not only enhances fault diagnosis efficiency but also offers a potent solution for similar multiclass imbalance challenges.