工业工程2025，Vol.28Issue(4)：77-88,12.DOI:10.3969/j.issn.1007-7375.250052
基于改进Jaya-RUSBoost模型的设备故障预测
Equipment Fault Prediction Based on an Improved Jaya-RUSBoost Model
摘要
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.关键词
故障预测/焊枪/数据不平衡/Jaya-RUSBoost/智能制造Key words
fault prediction/welding gun/data imbalance/Jaya-RUSBoost/intelligent manufacturing分类
信息技术与安全科学引用本文复制引用
李响,徐照光,吴建国..基于改进Jaya-RUSBoost模型的设备故障预测[J].工业工程,2025,28(4):77-88,12.基金项目
国家自然科学基金资助项目(72571042,72001034) （72571042,72001034）
辽宁省自然科学基金博士科研启动基金资助项目(2022-BS-088) （2022-BS-088）
中央高校基本科研业务费资助项目(DUT23RC(3)037) （DUT23RC(3）
大连理工大学经济管理学院研究生科研基金重点项目(DUTSEMPRFK02) （DUTSEMPRFK02）
