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Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function
This study aimed to propose a prognostic method based on a one-dimensional convolutional neural network (1-D CNN) with clustering loss by classification training. The 1-D CNN was trained by collecting the vibration signals of normal and malfunction data in hybrid loss function (i.e., classification...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349655/ https://www.ncbi.nlm.nih.gov/pubmed/32580465 http://dx.doi.org/10.3390/s20123539 |
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author | Lo, Chang-Cheng Lee, Ching-Hung Huang, Wen-Cheng |
author_facet | Lo, Chang-Cheng Lee, Ching-Hung Huang, Wen-Cheng |
author_sort | Lo, Chang-Cheng |
collection | PubMed |
description | This study aimed to propose a prognostic method based on a one-dimensional convolutional neural network (1-D CNN) with clustering loss by classification training. The 1-D CNN was trained by collecting the vibration signals of normal and malfunction data in hybrid loss function (i.e., classification loss in output and clustering loss in feature space). Subsequently, the obtained feature was adopted to estimate the status for prognosis. The open bearing dataset and established gear platform were utilized to validate the functionality and feasibility of the proposed model. Moreover, the experimental platform was used to simulate the gear mechanism of the semiconductor robot to conduct a practical experiment to verify the accuracy of the model estimation. The experimental results demonstrate the performance and effectiveness of the proposed method. |
format | Online Article Text |
id | pubmed-7349655 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73496552020-07-15 Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function Lo, Chang-Cheng Lee, Ching-Hung Huang, Wen-Cheng Sensors (Basel) Article This study aimed to propose a prognostic method based on a one-dimensional convolutional neural network (1-D CNN) with clustering loss by classification training. The 1-D CNN was trained by collecting the vibration signals of normal and malfunction data in hybrid loss function (i.e., classification loss in output and clustering loss in feature space). Subsequently, the obtained feature was adopted to estimate the status for prognosis. The open bearing dataset and established gear platform were utilized to validate the functionality and feasibility of the proposed model. Moreover, the experimental platform was used to simulate the gear mechanism of the semiconductor robot to conduct a practical experiment to verify the accuracy of the model estimation. The experimental results demonstrate the performance and effectiveness of the proposed method. MDPI 2020-06-22 /pmc/articles/PMC7349655/ /pubmed/32580465 http://dx.doi.org/10.3390/s20123539 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lo, Chang-Cheng Lee, Ching-Hung Huang, Wen-Cheng Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function |
title | Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function |
title_full | Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function |
title_fullStr | Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function |
title_full_unstemmed | Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function |
title_short | Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function |
title_sort | prognosis of bearing and gear wears using convolutional neural network with hybrid loss function |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349655/ https://www.ncbi.nlm.nih.gov/pubmed/32580465 http://dx.doi.org/10.3390/s20123539 |
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