哈尔滨工程大学学报2026，Vol.47Issue(6)：1271-1281,11.DOI:10.11990/jheu.202509006
基于多域特征融合和迁移学习的跨机器轴承故障诊断方法
Cross-machine bearing fault diagnosis method based on multi-domain fea-ture fusion and transfer learning
摘要
Abstract
Domain shift induced by inconsistent data distribution among heterogeneous equipment severely re-stricts the generalization performance of cross-machine bearing fault diagnosis models.A multi-domain feature fu-sion transfer learning network was proposed to enhance the model's generalization capability on target machines through knowledge transfer.The network integrated a backbone-freezing strategy with low-rank adaptation to im-prove parameter-update efficiency while avoiding the computational overhead associated with full-network fine-tuning.A multimodal feature fusion method based on the Hilbert transform and Gramian angular fields was devel-oped to jointly capture analytic signal characteristics and steady-transient dynamics,thereby constructing more dis-criminative composite fault representations.Six cross-machine diagnostic tasks built from three independent datas-ets demonstrated that the method achieved an average accuracy of up to 98.5%,significantly outperforming several state-of-the-art approaches while reducing trainable parameters by more than 90%compared with conventional full fine-tuning.Experimental results further demonstrated that the method exhibited strong cross-machine generaliza-tion.It is well suited for industrial fault diagnosis under cross-domain conditions and provides a robust solution for predictive maintenance of manufacturing equipment.关键词
轴承故障诊断/跨机器/领域自适应/迁移学习/LoRA/多域特征融合/格拉姆角场/希尔伯特变换Key words
bearing fault diagnosis/cross-machine/domain adaptation/transfer learning/LoRA/multi-domain feature fusion/Gramian angular field/Hilbert transform分类
机械制造引用本文复制引用
谢秀煌,俞炅旻,符栋梁,高伟..基于多域特征融合和迁移学习的跨机器轴承故障诊断方法[J].哈尔滨工程大学学报,2026,47(6):1271-1281,11.基金项目
国家自然科学基金项目(62171205). （62171205）
