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CNN Hardware Accelerator for Real-Time Bearing Fault Diagnosis
This paper introduces a one-dimensional convolutional neural network (CNN) hardware accelerator. It is crafted to conduct real-time assessments of bearing conditions using economical hardware components, implemented on a field-programmable gate array evaluation platform, negating the necessity to tr...
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/PMC10346166/ https://www.ncbi.nlm.nih.gov/pubmed/37447743 http://dx.doi.org/10.3390/s23135897 |
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author | Chung, Ching-Che Liang, Yu-Pei Jiang, Hong-Jin |
author_facet | Chung, Ching-Che Liang, Yu-Pei Jiang, Hong-Jin |
author_sort | Chung, Ching-Che |
collection | PubMed |
description | This paper introduces a one-dimensional convolutional neural network (CNN) hardware accelerator. It is crafted to conduct real-time assessments of bearing conditions using economical hardware components, implemented on a field-programmable gate array evaluation platform, negating the necessity to transfer data to a cloud-based server. The adoption of the down-sampling technique augments the visible time span of the signal in an image, thereby enhancing the accuracy of the bearing condition diagnosis. Furthermore, the proposed method of quaternary quantization enhances precision and shrinks the memory demand for the neural network model by an impressive 89%. Provided that the current signal data sampling rate stands at 64 K samples/s, the proposed design can accomplish real-time fault diagnosis at a clock frequency of 100 MHz. Impressively, the response duration of the proposed CNN hardware system is a mere 0.28 s, with the fault diagnosis precision reaching a remarkable 96.37%. |
format | Online Article Text |
id | pubmed-10346166 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103461662023-07-15 CNN Hardware Accelerator for Real-Time Bearing Fault Diagnosis Chung, Ching-Che Liang, Yu-Pei Jiang, Hong-Jin Sensors (Basel) Article This paper introduces a one-dimensional convolutional neural network (CNN) hardware accelerator. It is crafted to conduct real-time assessments of bearing conditions using economical hardware components, implemented on a field-programmable gate array evaluation platform, negating the necessity to transfer data to a cloud-based server. The adoption of the down-sampling technique augments the visible time span of the signal in an image, thereby enhancing the accuracy of the bearing condition diagnosis. Furthermore, the proposed method of quaternary quantization enhances precision and shrinks the memory demand for the neural network model by an impressive 89%. Provided that the current signal data sampling rate stands at 64 K samples/s, the proposed design can accomplish real-time fault diagnosis at a clock frequency of 100 MHz. Impressively, the response duration of the proposed CNN hardware system is a mere 0.28 s, with the fault diagnosis precision reaching a remarkable 96.37%. MDPI 2023-06-25 /pmc/articles/PMC10346166/ /pubmed/37447743 http://dx.doi.org/10.3390/s23135897 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 Chung, Ching-Che Liang, Yu-Pei Jiang, Hong-Jin CNN Hardware Accelerator for Real-Time Bearing Fault Diagnosis |
title | CNN Hardware Accelerator for Real-Time Bearing Fault Diagnosis |
title_full | CNN Hardware Accelerator for Real-Time Bearing Fault Diagnosis |
title_fullStr | CNN Hardware Accelerator for Real-Time Bearing Fault Diagnosis |
title_full_unstemmed | CNN Hardware Accelerator for Real-Time Bearing Fault Diagnosis |
title_short | CNN Hardware Accelerator for Real-Time Bearing Fault Diagnosis |
title_sort | cnn hardware accelerator for real-time bearing fault diagnosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346166/ https://www.ncbi.nlm.nih.gov/pubmed/37447743 http://dx.doi.org/10.3390/s23135897 |
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