Few-Shot Rolling Bearing Fault Diagnosis with Metric-Based Meta Learning
School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(22), 6437; https://doi.org/10.3390/s20226437
Received: 13 October 2020
/
Revised: 5 November 2020
/
Accepted: 10 November 2020
/
Published: 11 November 2020
(This article belongs to the Special Issue Deep Learning, Artificial Neural Networks and Sensors for Fault Diagnosis)
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.
View Full-Text
Keywords:
deep learning; few-shot learning; meta learning; convolutional neural network; bearing fault diagnosis
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Wang, S.; Wang, D.; Kong, D.; Wang, J.; Li, W.; Zhou, S. Few-Shot Rolling Bearing Fault Diagnosis with Metric-Based Meta Learning. Sensors 2020, 20, 6437. https://doi.org/10.3390/s20226437
AMA Style
Wang S, Wang D, Kong D, Wang J, Li W, Zhou S. Few-Shot Rolling Bearing Fault Diagnosis with Metric-Based Meta Learning. Sensors. 2020; 20(22):6437. https://doi.org/10.3390/s20226437
Chicago/Turabian StyleWang, Sihan, Dazhi Wang, Deshan Kong, Jiaxing Wang, Wenhui Li, and Shuai Zhou. 2020. "Few-Shot Rolling Bearing Fault Diagnosis with Metric-Based Meta Learning" Sensors 20, no. 22: 6437. https://doi.org/10.3390/s20226437
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
