Few-Shot Rolling Bearing Fault Diagnosis with Metric-Based Meta Learning

Sensors

卷:20

Deshan Kong¹  Jiaxing Wang¹  Dazhi Wang¹  Shuai Zhou¹  Sihan Wang¹  Wenhui Li¹ 

[1] School of Information Science and Engineering, Northeastern University, Shenyang 110819, China;

关键词: deep learning;    few-shot learning;    meta learning;    convolutional neural network;    bearing fault diagnosis;   

DOI  :  10.3390/s20226437

来源: DOAJ

【 摘 要 】

Fault diagnosis methods based on deep learning and big data have achieved good results on rotating machinery. However, the conventional deep learning method of bearing fault diagnosis is mostly based on laboratory artificial simulation data, and there is an error with actual fault data, which will reduce the generalization performance of the deep learning method. In addition, labeled data are very precious in real industrial environment. Due to expensive equipment and personnel safety issues, it is difficult to obtain a large amount of high-quality fault labeling data. Therefore, in this paper, we propose a metric-based meta-learning method named Reinforce Relation Network (RRN) for diagnosing bearing faults with few-shot samples. In the proposed method, a 1D convolution neural network is used to extract fault features, and a metric learner is used to predict the similarity between samples under different transfer conditions. Label smoothing and the Adabound algorithm are utilized to further improve the performance of network classification. The performance of the proposed method is verified on a dataset which contains artificial damage and natural damage data. The comparison studies with other methods demonstrate the superiority of the proposed method in the few-shot scenario.

【 授权许可】

Unknown