噪声与振动控制2025，Vol.45Issue(3)：105-112,8.DOI:10.3969/j.issn.1006-1355.2025.03.017
转子裂纹故障诊断与特征迁移模型研究
Rotor Crack Fault Diagnosis and Feature Transfer Model
摘要
Abstract
Aiming at the deficiencies in the research of the intelligent diagnosis model for rotor crack faults,such as the requirement for a large number of data samples as the support,poor data re-usability,and the impossibility to identify the propagation situation of crack,a data-driven domain adaptation transfer learning model was proposed.In this model,based on triplet loss and denoising autoencoder network,domain-invariant crack fault features were extracted using adversarial training strategy to achieve global alignment of features from different domains.The triplet loss was employed to constrain the extracted fault features and realize class-level feature alignment across different domains.With the vibration signal of cracked rotor as input,the propagation stage of crack was predicted.Finally,the cross-operating condition feature transfer performance of the model was tested.The results show that the average prediction accuracy of 10 different cross-operating condition feature transfer tasks reaches 91.3%.Comparing with other classical transfer learning models,the proposed model can extract more effective domain-invariant crack fault features and exhibit stronger feature transfer generalization capabilities.关键词
故障诊断/裂纹转子/迁移学习/域适应理论/三元组损失Key words
fault diagnosis/cracked rotor/transfer learning/domain adaptation theory/triplet loss分类
机械制造引用本文复制引用
陈志昊,赵文强,王正伟,周军,石生超,李富才..转子裂纹故障诊断与特征迁移模型研究[J].噪声与振动控制,2025,45(3):105-112,8.基金项目
国网青海省电力公司科技资助项目(522807230005) （522807230005）
