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Artificial Intelligence of Manufacturing Robotics Health Monitoring System by Semantic Modeling

Robotics is widely used in nearly all sorts of manufacturing. Steady performance and accurate movement of robotics are vital in quality control. Along with the coming of the Industry 4.0 era, oceans of sensor data from robotics are available, within which the health condition and faults are enclosed...

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Detalles Bibliográficos
Autores principales: Sun, Han, Yang, Yuan, Yu, Jiachuan, Zhang, Zhisheng, Xia, Zhijie, Zhu, Jianxiong, Zhang, Hui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8878482/
https://www.ncbi.nlm.nih.gov/pubmed/35208424
http://dx.doi.org/10.3390/mi13020300
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author Sun, Han
Yang, Yuan
Yu, Jiachuan
Zhang, Zhisheng
Xia, Zhijie
Zhu, Jianxiong
Zhang, Hui
author_facet Sun, Han
Yang, Yuan
Yu, Jiachuan
Zhang, Zhisheng
Xia, Zhijie
Zhu, Jianxiong
Zhang, Hui
author_sort Sun, Han
collection PubMed
description Robotics is widely used in nearly all sorts of manufacturing. Steady performance and accurate movement of robotics are vital in quality control. Along with the coming of the Industry 4.0 era, oceans of sensor data from robotics are available, within which the health condition and faults are enclosed. Considering the growing complexity of the manufacturing system, an automatic and intelligent health-monitoring system is required to detect abnormalities of robotics in real-time to promote quality and reduce safety risks. Therefore, in this study, we designed a novel semantic-based modeling method for multistage robotic systems. Experiments show that sole modeling is not sufficient for multiple stages. We propose a descriptor to conclude the stages of robotic systems by learning from operational data. The descriptors are akin to a vocabulary of the systems; hence, semantic checking can be carried out to monitor the correctness of operations. Furthermore, the stage classification and its semantics were used to apply various regression models to each stage to monitor the quality of each operation. The proposed method was applied to a photovoltaic manufacturing system. Benchmarks on production datasets from actual factories show the effectiveness of the proposed method to realize an AI-enabled real-time health-monitoring system of robotics.
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spelling pubmed-88784822022-02-26 Artificial Intelligence of Manufacturing Robotics Health Monitoring System by Semantic Modeling Sun, Han Yang, Yuan Yu, Jiachuan Zhang, Zhisheng Xia, Zhijie Zhu, Jianxiong Zhang, Hui Micromachines (Basel) Article Robotics is widely used in nearly all sorts of manufacturing. Steady performance and accurate movement of robotics are vital in quality control. Along with the coming of the Industry 4.0 era, oceans of sensor data from robotics are available, within which the health condition and faults are enclosed. Considering the growing complexity of the manufacturing system, an automatic and intelligent health-monitoring system is required to detect abnormalities of robotics in real-time to promote quality and reduce safety risks. Therefore, in this study, we designed a novel semantic-based modeling method for multistage robotic systems. Experiments show that sole modeling is not sufficient for multiple stages. We propose a descriptor to conclude the stages of robotic systems by learning from operational data. The descriptors are akin to a vocabulary of the systems; hence, semantic checking can be carried out to monitor the correctness of operations. Furthermore, the stage classification and its semantics were used to apply various regression models to each stage to monitor the quality of each operation. The proposed method was applied to a photovoltaic manufacturing system. Benchmarks on production datasets from actual factories show the effectiveness of the proposed method to realize an AI-enabled real-time health-monitoring system of robotics. MDPI 2022-02-14 /pmc/articles/PMC8878482/ /pubmed/35208424 http://dx.doi.org/10.3390/mi13020300 Text en © 2022 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
Sun, Han
Yang, Yuan
Yu, Jiachuan
Zhang, Zhisheng
Xia, Zhijie
Zhu, Jianxiong
Zhang, Hui
Artificial Intelligence of Manufacturing Robotics Health Monitoring System by Semantic Modeling
title Artificial Intelligence of Manufacturing Robotics Health Monitoring System by Semantic Modeling
title_full Artificial Intelligence of Manufacturing Robotics Health Monitoring System by Semantic Modeling
title_fullStr Artificial Intelligence of Manufacturing Robotics Health Monitoring System by Semantic Modeling
title_full_unstemmed Artificial Intelligence of Manufacturing Robotics Health Monitoring System by Semantic Modeling
title_short Artificial Intelligence of Manufacturing Robotics Health Monitoring System by Semantic Modeling
title_sort artificial intelligence of manufacturing robotics health monitoring system by semantic modeling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8878482/
https://www.ncbi.nlm.nih.gov/pubmed/35208424
http://dx.doi.org/10.3390/mi13020300
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