Set up a digital-twin diagnostic model with deep learning

This paper addresses the resource-intensive nature of traditional intelligent fault diagnosis by proposing a computationally efficient yet highly effective digital twin diagnostic model. We transform time-series fault data into 2D image-like representations, known as Gramian Angular Fields (GAF), an...

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Bibliographic Details
Published in2024 IEEE 4th International Conference on Digital Twins and Parallel Intelligence (DTPI) pp. 27 - 31
Main Authors Liu, Yijiao, Huo, Mingying, He, Long, Li, Ming, Xue, Yufeng, Qi, Naiming
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.10.2024
Subjects
Adaptation models
Computational efficiency
Computational modeling
Convolution
Convolutional neural networks
Data mining
Deep Learning(DL)
Digital twins
Digital-twin diagnostic model
Fault diagnosis
Feature extraction
Image coding
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DOI10.1109/DTPI61353.2024.10778897

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Summary:This paper addresses the resource-intensive nature of traditional intelligent fault diagnosis by proposing a computationally efficient yet highly effective digital twin diagnostic model. We transform time-series fault data into 2D image-like representations, known as Gramian Angular Fields (GAF), and then integrate octave convolution into the ResNet50 architecture to extract robust features from machine data. By leveraging the lower complexity of octave convolution, our approach significantly enhances diagnostic efficiency. Experimental results demonstrate that our method achieves over 95% accuracy while reducing computational costs by 42%. And this algorithm can be used for lightweight and efficient fault diagnosis.
DOI:10.1109/DTPI61353.2024.10778897