Robust Supervised Contrastive Learning for Fault Diagnosis Under Different Noises and Conditions

Hu, Chenye; Wu, Jingyao; Sun, Chuang; Yan, Ruqiang; Chen, Xuefeng

doi:10.1109/icsmd53520.2021.9670794

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…In the proposed SCL model, we propose four augmentation techniques, namely Gaussian noise adding, Gaussian noise scaling, random cropping, and phase shifting. Data augmentation is performed to increase the number of training data for fault diagnosis [34].…”

Section: Data Augmentationmentioning

confidence: 99%

Supervised Contrastive Learning for Fault Diagnosis Based on Phase-Resolved Partial Discharge in Gas-Insulated Switchgear

Dang,

Ho,

Vo-Nguyen

et al. 2023

Energies

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Supervised contrastive learning (SCL) has recently emerged as an alternative to conventional machine learning and deep neural networks. In this study, we propose an SCL model with data augmentation techniques using phase-resolved partial discharge (PRPD) in gas-insulated switchgear (GIS). To increase the fault data for training, we employ Gaussian noise adding, Gaussian noise scaling, random cropping, and phase shifting for supervised contrastive loss. The performance of the proposed SCL was verified by four types of faults in the GIS and on-site noise using an on-line ultra-high-frequency (UHF) partial discharge (PD) monitoring system. The experimental results show that the proposed SCL achieves a classification accuracy of 97.28% and outperforms the other algorithms, including support vector machines (SVM), multilayer perceptron (MLP), and convolution neural networks (CNNs) in terms of classification accuracy, by 6.8%, 4.28%, 2.04%, respectively.

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Section: Data Augmentationmentioning

confidence: 99%

Supervised Contrastive Learning for Fault Diagnosis Based on Phase-Resolved Partial Discharge in Gas-Insulated Switchgear

Dang,

Ho,

Vo-Nguyen

et al. 2023

Energies

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Robust Classification Model for Diabetic Retinopathy Based on the Contrastive Learning Method with a Convolutional Neural Network

Feng

Zhang

et al. 2022

Applied Sciences

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Diabetic retinopathy is one of the most common microvascular complications of diabetes. Early detection and treatment can effectively reduce the risk. Hence, a robust computer-aided diagnosis model is important. Based on the labeled fundus images, we build a binary classification model based on ResNet-18 and transfer learning and, more importantly, improve the robustness of the model through supervised contrastive learning. The model is tested with different learning rates and data augmentation methods. The standard deviations of the multiple test results decrease from 4.11 to 0.15 for different learning rates and from 1.53 to 0.18 for different data augmentation methods. In addition, the supervised contrastive learning method also improves the average accuracy of the model, which increases from 80.7% to 86.5%.

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Robust Supervised Contrastive Learning for Fault Diagnosis Under Different Noises and Conditions

Cited by 2 publications

References 10 publications

Supervised Contrastive Learning for Fault Diagnosis Based on Phase-Resolved Partial Discharge in Gas-Insulated Switchgear

Supervised Contrastive Learning for Fault Diagnosis Based on Phase-Resolved Partial Discharge in Gas-Insulated Switchgear

Robust Classification Model for Diabetic Retinopathy Based on the Contrastive Learning Method with a Convolutional Neural Network

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