Imbalanced Fault Diagnosis by Supervised Contrastive Learning
International Conference on Computer Supported Cooperative Work in Design (CSCWD)(2022)
摘要
Intelligent fault diagnosis is essential to guarantee the safe operation of industrial processes. And an important issue is how to develop a method to tackle the dilemma where we can only collect limited fault samples. In this paper, we propose a two-stage method based on supervised contrastive learning for imbalanced fault diagnosis tasks. We utilize the supervised contrastive learning technique as it has shown a powerful representation learning ability in previous works. The computational experiments on the Tennessee Eastman dataset show that our proposed two-stage method can achieve improved performance when compared to existing methods.
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关键词
Fault diagnosis,Supervised contrastive Learning,Imbalanced learning,Deep learning
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