A Quadruplet Deep Metric Learning model for imbalanced time-series fault diagnosis

Deep learning based methods are attractive and meaningful in the field of fault diagnosis recently. However, the sample size of different faults may be imbalanced in practical application scenarios, which results in performance degeneration. The optimization of data representation could be an effective way to alleviate such phenomenon. In this paper, a deep learning framework considering time series and distance metric called LSTM-Quadruplet Deep Metric Learning(LSTM-QDM) model is proposed. It maps original space to a feature space where the distribution of imbalance faults is more distinguishable. A novel quadruplet data pair is designed which adds a minor sample from imbalanced classes into traditional data pair. Based on such data pair, a quadruplet loss function is proposed to increase the distance between the imbalanced classes and other classes, and its combination with softmax loss would improve the representation ability and classification performance simultaneously. The proposed data pair and loss function encourage the full mining of imbalanced data in the training process. The experiments are carried out on two open-source datasets including TE and CWRU, and the fault diagnosis performance is validated under different imbalanced conditions. The experimental results indicate that our proposed model is effective and robust in the imbalanced fault diagnosis task. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a deep learning framework for fault diagnosis that addresses the issue of imbalanced samples. It introduces a novel data pair and loss function to improve classification performance and encourage mining of imbalanced data.