Journal
IEEE ACCESS
Volume 7, Issue -, Pages 110895-110904Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2934233
Keywords
Deep learning; few-shot learning; bearing fault diagnosis; limited data
Categories
Funding
- National Natural Science Foundation of China [91746116]
- New Model Application Project of National Intelligent Manufacturing [[2017] 468]
- Science and Technology Project of Guizhou Province [[2015] 4011, [2016] 5013, [2017] 5788]
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This paper focuses on bearing fault diagnosis with limited training data. A major challenge in fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type under all working conditions. Recently deep learning based fault diagnosis methods have achieved promising results. However, most of these methods require large amount of training data. In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data. Our model is based on the siamese neural network, which learns by exploiting sample pairs of the same or different categories. Experimental results over the standard Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset showed that our few-shot learning approach is more effective in fault diagnosis with limited data availability. When tested over different noise environments with minimal amount of training data, the performance of our few-shot learning model surpasses the one of the baseline with reasonable noise level. When evaluated over test sets with new fault types or new working conditions, few-shot models work better than the baseline trained with all fault types.
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