Augmentation-based discriminative meta-learning for cross-machine few-shot fault diagnosis  

作　　者：XIA PengCheng HUANG YiXiang WANG YuXiang LIU ChengLiang LIU Jie 

机构地区：[1]State Key Laboratory of Mechanical System and Vibration,Shanghai Jiao Tong University,Shanghai 200240,China [2]MoE Key Lab of Artificial Intelligence,AI Institute,Shanghai Jiao Tong University,Shanghai 200240,China [3]Department of Mechanical and Aerospace Engineering,Carleton University,Ottawa ON K1S 5B6,Canada

出　　处：《Science China(Technological Sciences)》2023年第6期1698-1716,共19页中国科学（技术科学英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant No.51975356);Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0102)。

摘　　要：Deep learning methods have demonstrated promising performance in fault diagnosis tasks.Although the scarcity of data in industrial scenarios limits the practical application of such methods,transfer learning effectively tackles this issue through crossmachine knowledge transfer.Nevertheless,the cross-machine few-shot problem,which is a more general industrial scenario,has been rarely investigated.Existing studies have not considered the cross-machine domain shift problem,which results in poor testing performance.This paper proposes an augmentation-based discriminative meta-learning method to address this issue.In the meta-training process,signal transformation is proposed to increase the meta-task diversity for more robust feature learning,and multi-scale learning is combined for more adaptive feature embedding.In the meta-testing process,limited labeled fault information is used to promote model generalization in the target domain through quasi-meta-training based on data augmentation.Furthermore,a novel hyperbolic prototypical loss is proposed for more discriminative feature representation and separable category prototypes by designing a hyperbolic decision boundary.Cross-machine few-shot diagnosis experiments were conducted using three datasets from different machines,namely,the bearing,motor,and gear datasets.The effectiveness of the proposed method was verified through ablation and comparison studies.

关 键 词：fault diagnosis few-shot learning META-LEARNING data augmentation cross-machine discriminative loss 

分 类 号：TH17[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]