机械科学与技术2026，Vol.45Issue(7)：1173-1184,12.DOI:10.13433/j.cnki.1003-8728.20240153
一种动态稀疏化的域增量轴承故障诊断方法
A Dynamic Sparsification Method for Domain Incremental Bearing Fault Diagnosis
摘要
Abstract
Industrial scenarios with continuous dynamic changes in rotational speed and load maybe lead to the failure of already trained models,thus degrading the performance of bearing fault diagnosis.To allow the model to learn new domain knowledge while effectively retaining the old domain knowledge that has already been learned,a dynamic sparsification method for domain incremental bearing fault diagnosis is proposed.Firstly,the L1-norm-based dynamic screening module for representative samples is utilized to select representative samples that contain important knowledge in different domains,and protect previously learned knowledge in different domains while learning new knowledge.Secondly,a sparsity rate parameter,which can be set autonomously according to the storage requirements,is introduced during the screening process to adjust the number of representative samples to be stored to save memory space.Finally,catastrophic forgetting is avoided by using the importance loss function to impose constraints on important parameters in the old domain.The proposed method is used to experiments on two rotating machinery datasets,and the results show that the proposed method achieves the highest average diagnostic accuracies of 100％and 99.06％at the same and different rotational speeds(loads),respectively.The memory capability of the model is also superior to traditional fault diagnosis methods.关键词
轴承故障诊断/域增量/灾难性遗忘/L1-norm/重要度损失函数Key words
bearing fault diagnosis/domain incremental/catastrophic forgetting/L1-norm/importance loss function分类
信息技术与安全科学引用本文复制引用
肖鑫淼,孙梅迪,陈棠燕,何青..一种动态稀疏化的域增量轴承故障诊断方法[J].机械科学与技术,2026,45(7):1173-1184,12.基金项目
国家自然科学基金项目(62103063)、湖南省教育厅科研项目(22C0141)及长沙理工大学研究生科研创新项目(CSLGCX23070) （62103063）
