Equipment Fault Prediction Based on an Improved Jaya-RUSBoost Model
LI Xiang
XU Zhaoguang
WU Jianguo
Abstract：As a critical piece of equipment in engineering manufacturing,the stability and reliability of welding guns are crucial for the continuity of production lines and the quality of products.To address the challenge of data imbalance in welding gun fault prediction,a welding gun fault prediction method based on an improved Jaya-RUSBoost model is proposed.By combining undersampling,ensemble learning,and parameter setting optimization,this method achieves data balance and improves the accuracy of fault prediction.First,a RUSBoost fault prediction model is constructed,and experiments are designed to evaluate the impact of hyperparameters on model performance,thereby determining the optimal range of model parameters.Subsequently,the Jaya metaheuristic algorithm is employed to iteratively optimize the parameters of the RUSBoost model to obtain the optimal parameter configuration of fault prediction.The results of the case study show that compared with the traditional RUSBoost algorithm,the proposed algorithm improves the average fault prediction accuracy and F1-score by 9.43%and 8.41%,respectively,across five welding guns.Moreover,compared with various machine learning models,the accuracy and other indicators are also significantly improved.The proposed method in this paper offers high practical value and broad prospects for promotion,providing effective support for the intelligent maintenance of welding equipment.
Keywords：fault predictionwelding gundata imbalanceJaya-RUSBoostintelligent manufacturing
Publication Date：2025-08-30
Online Publishing Date：2025-09-22(First online date of this platform, not the publication date of the document)
Pages：12( 77-88 )