Before revision:  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 .
After revision:  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 model, where imbalanced fault data and a 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 .