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a novel deep metric learning model for imbalanced fault diagnosis and toward open-set classification

Sagot :

Intelligent fault diagnosis based on deep neural networks and big data has been an attractive field and shows great prospects for applications.

What is novel deep metric learning model?

Unbalanced fault data and a quadruplet data pair design approach are taken into account in the unique deep metric learning model that is proposed, which may effectively and robustly enhance the performance of unbalanced fault detection.

What is intelligent diagnosis?

In recent years, the field of intelligent diagnosis methods based on data-driven and deep learning has gained popularity and significance. However, the imbalance of time-series defect is a critical issue that needs to be resolved in scenarios of practical application. This research suggests a novel deep metric learning model that takes into account quadruplet data pair design and imbalanced fault data. Based on this data pair, a quadruplet loss function that considers both the intra-class data distribution and the inter-class distance is proposed. This quadruplet loss takes imbalanced sample pairs into particular consideration.

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