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Convolutional Neural Network-Based Transformer Fault Diagnosis Using Vibration Signals
Fast and accurate fault diagnosis is crucial to transformer safety and cost-effectiveness. Recently, vibration analysis for transformer fault diagnosis is attracting increasing attention due to its ease of implementation and low cost, while the complex operating environment and loads of transformers...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223529/ https://www.ncbi.nlm.nih.gov/pubmed/37430695 http://dx.doi.org/10.3390/s23104781 |
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author | Li, Chao Chen, Jie Yang, Cheng Yang, Jingjian Liu, Zhigang Davari, Pooya |
author_facet | Li, Chao Chen, Jie Yang, Cheng Yang, Jingjian Liu, Zhigang Davari, Pooya |
author_sort | Li, Chao |
collection | PubMed |
description | Fast and accurate fault diagnosis is crucial to transformer safety and cost-effectiveness. Recently, vibration analysis for transformer fault diagnosis is attracting increasing attention due to its ease of implementation and low cost, while the complex operating environment and loads of transformers also pose challenges. This study proposed a novel deep-learning-enabled method for fault diagnosis of dry-type transformers using vibration signals. An experimental setup is designed to simulate different faults and collect the corresponding vibration signals. To find out the fault information hidden in the vibration signals, the continuous wavelet transform (CWT) is applied for feature extraction, which can convert vibration signals to red-green-blue (RGB) images with the time–frequency relationship. Then, an improved convolutional neural network (CNN) model is proposed to complete the image recognition task of transformer fault diagnosis. Finally, the proposed CNN model is trained and tested with the collected data, and its optimal structure and hyperparameters are determined. The results show that the proposed intelligent diagnosis method achieves an overall accuracy of 99.95%, which is superior to other compared machine learning methods. |
format | Online Article Text |
id | pubmed-10223529 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102235292023-05-28 Convolutional Neural Network-Based Transformer Fault Diagnosis Using Vibration Signals Li, Chao Chen, Jie Yang, Cheng Yang, Jingjian Liu, Zhigang Davari, Pooya Sensors (Basel) Article Fast and accurate fault diagnosis is crucial to transformer safety and cost-effectiveness. Recently, vibration analysis for transformer fault diagnosis is attracting increasing attention due to its ease of implementation and low cost, while the complex operating environment and loads of transformers also pose challenges. This study proposed a novel deep-learning-enabled method for fault diagnosis of dry-type transformers using vibration signals. An experimental setup is designed to simulate different faults and collect the corresponding vibration signals. To find out the fault information hidden in the vibration signals, the continuous wavelet transform (CWT) is applied for feature extraction, which can convert vibration signals to red-green-blue (RGB) images with the time–frequency relationship. Then, an improved convolutional neural network (CNN) model is proposed to complete the image recognition task of transformer fault diagnosis. Finally, the proposed CNN model is trained and tested with the collected data, and its optimal structure and hyperparameters are determined. The results show that the proposed intelligent diagnosis method achieves an overall accuracy of 99.95%, which is superior to other compared machine learning methods. MDPI 2023-05-16 /pmc/articles/PMC10223529/ /pubmed/37430695 http://dx.doi.org/10.3390/s23104781 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Chao Chen, Jie Yang, Cheng Yang, Jingjian Liu, Zhigang Davari, Pooya Convolutional Neural Network-Based Transformer Fault Diagnosis Using Vibration Signals |
title | Convolutional Neural Network-Based Transformer Fault Diagnosis Using Vibration Signals |
title_full | Convolutional Neural Network-Based Transformer Fault Diagnosis Using Vibration Signals |
title_fullStr | Convolutional Neural Network-Based Transformer Fault Diagnosis Using Vibration Signals |
title_full_unstemmed | Convolutional Neural Network-Based Transformer Fault Diagnosis Using Vibration Signals |
title_short | Convolutional Neural Network-Based Transformer Fault Diagnosis Using Vibration Signals |
title_sort | convolutional neural network-based transformer fault diagnosis using vibration signals |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223529/ https://www.ncbi.nlm.nih.gov/pubmed/37430695 http://dx.doi.org/10.3390/s23104781 |
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