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

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.