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.


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    Title :

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


    Contributors:
    Gen Li (author) / Wenhai Li (author) / Tianzhu Wen (author) / Weichao Sun (author) / Xi Tang (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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