福州大学学报（自然科学版）2026，Vol.54Issue(1)：38-44,7.DOI:10.7631/issn.1000-2243.25036
改进EBCLS模型在滚动轴承故障预测中的应用
Application of improved EBCLS model in rolling bearing fault prediction
摘要
Abstract
Aiming at the problem of long training time and low accuracy in predicting rolling bearing faults of high-speed locomotives,an enhanced width convolutional learning system(EBCLS)based on fast iterative fusion of residual learning with enhanced nodes is proposed.The system first extracts signal features by combining the broad learning system(BLS)with convolutional neural networks,and continuously optimizes and updates weights by integrating residual learning and adding enhanced nodes during the training process.Finally,the trained model is used to predict the data with sliding windows and output the prediction results.The proposed method was validated and compared with other BLS methods for prediction results,and the results showed that this method exhibited better real-time prediction performance while improving prediction accuracy.关键词
滚动轴承/故障预测/高速机车/卷积特征/滑动时间窗Key words
rolling bearing/fault prediction/high-speed locomotive/convolutional features/sliding time window分类
信息技术与安全科学引用本文复制引用
林金亮,刘暾东,张馨月,张泽华..改进EBCLS模型在滚动轴承故障预测中的应用[J].福州大学学报（自然科学版）,2026,54(1):38-44,7.基金项目
国家自然科学基金资助项目(52475609) （52475609）
福建省教育厅中青年教师教育科研资助项目(JAT210903) （JAT210903）
龙岩市科技计划重点资助项目(2022LYF9007) （2022LYF9007）
