Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Chung, Ching-Che, Liang, Yu-Pei, Jiang, Hong-Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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
_version_ 1785073250032680960
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
work_keys_str_mv AT chungchingche cnnhardwareacceleratorforrealtimebearingfaultdiagnosis
AT liangyupei cnnhardwareacceleratorforrealtimebearingfaultdiagnosis
AT jianghongjin cnnhardwareacceleratorforrealtimebearingfaultdiagnosis