Quadruplet Deep Metric Learning Model for Imbalanced Time-series Fault Diagnosis

• URL: http://arxiv.org/abs/2107.03786v1
• Date: Thu, 8 Jul 2021 11:56:41 GMT
• Title: Quadruplet Deep Metric Learning Model for Imbalanced Time-series Fault Diagnosis
• Authors: Xingtai Gui, Jiyang Zhang
• Abstract summary: This paper analyzes how to improve the performance of imbalanced classification by adjusting the distance between classes and the distribution within a class. A novel quadruplet data pair design considering imbalance class is proposed with reference to traditional deep metric learning. The reasonable combination of quadruplet loss and softmax loss function can reduce the impact of imbalance.
• Score: 0.2538209532048866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent diagnosis method based on data-driven and deep learning is an attractive and meaningful field in recent years. However, in practical application scenarios, the imbalance of time-series fault is an urgent problem to be solved. From the perspective of Bayesian probability, this paper analyzes how to improve the performance of imbalanced classification by adjusting the distance between classes and the distribution within a class and proposes a time-series fault diagnosis model based on deep metric learning. As a core of deep metric learning, a novel quadruplet data pair design considering imbalance class is proposed with reference to traditional deep metric learning. Based on such data pair, this paper proposes a quadruplet loss function which takes into account the inter-class distance and the intra-class data distribution, and pays special attention to imbalanced sample pairs. The reasonable combination of quadruplet loss and softmax loss function can reduce the impact of imbalance. Experiments on two open datasets are carried out to verify the effectiveness and robustness of the model. Experimental results show that the proposed method can effectively improve the performance of imbalanced classification.

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