Center Loss Guided Prototypical Networks for Unbalance Few-Shot Industrial Fault Diagnosis

The success of deep learning is based on a large number of tagged data, which is challenging to satisfy on many occasions. Especially in industry fault diagnosis, considering the cost of data collection, the fault data are few and severely unbalanced. Therefore, it is not enough to support a reliable data-driven deep learning model. Few-shot learning effectively solves the few sample problems, but traditional methods pay little attention to the impact of unbalanced data. However, imbalanced data exists in large quantities. At the same time, unbalanced data often causes decision boundaries to be biased towards categories with larger sample sizes, resulting in lower accuracy. This study proposes a prototype network incorporating center loss for diagnosing industrial faults with few-shot samples. Based on the prototypical networks, by adding center loss at the loss level, the mapping points of the samples in the feature space play the role of intraclass contraction and interclass separation, thereby improving the classification effect. The experiment takes the TE process industrial data set as an example. Comparing various current few-shot learning methods reflects the superiority of the method proposed in the few-shot imbalanced scenario.