工业4.0机器状态预测的新方法——以弹簧厂机器为例

Tzu-Yu Lin, Yo-Ming Chen, Don-Lin Yang, Yi-Chung Chen
{"title":"工业4.0机器状态预测的新方法——以弹簧厂机器为例","authors":"Tzu-Yu Lin, Yo-Ming Chen, Don-Lin Yang, Yi-Chung Chen","doi":"10.1109/ICS.2016.0071","DOIUrl":null,"url":null,"abstract":"In response to the technological development in recent years, many technology giants are making efforts toward Industry 4.0. However, many small-and medium-sized factories cannot even computerize and automate their factories, which is the foundation of Industry 4.0, due to inadequate capital and scale. This is because the majority of these factories are still using conventional machines in which the data cannot be digitized. Consequently, they cannot achieve the goal of Industry 4.0. This work therefore proposes a simple approach that facilitates the transition of these small-and medium-sized factories. The approach uses add-on triaxial sensors to aid in machine monitoring. The data obtained is analyzed for abnormalities using neural networks. Experiment results demonstrate the validity of the proposed approach.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"New Method for Industry 4.0 Machine Status Prediction - A Case Study with the Machine of a Spring Factory\",\"authors\":\"Tzu-Yu Lin, Yo-Ming Chen, Don-Lin Yang, Yi-Chung Chen\",\"doi\":\"10.1109/ICS.2016.0071\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In response to the technological development in recent years, many technology giants are making efforts toward Industry 4.0. However, many small-and medium-sized factories cannot even computerize and automate their factories, which is the foundation of Industry 4.0, due to inadequate capital and scale. This is because the majority of these factories are still using conventional machines in which the data cannot be digitized. Consequently, they cannot achieve the goal of Industry 4.0. This work therefore proposes a simple approach that facilitates the transition of these small-and medium-sized factories. The approach uses add-on triaxial sensors to aid in machine monitoring. The data obtained is analyzed for abnormalities using neural networks. Experiment results demonstrate the validity of the proposed approach.\",\"PeriodicalId\":281088,\"journal\":{\"name\":\"2016 International Computer Symposium (ICS)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Computer Symposium (ICS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICS.2016.0071\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Computer Symposium (ICS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICS.2016.0071","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

摘要

为了应对近年来的技术发展,许多科技巨头都在朝着工业4.0的方向努力。然而,由于资金和规模不足,许多中小型工厂甚至无法实现工厂的计算机化和自动化,而这正是工业4.0的基础。这是因为这些工厂中的大多数仍在使用传统的机器,其中的数据无法数字化。因此,他们无法实现工业4.0的目标。因此,这项工作提出了一种简单的方法,可以促进这些中小型工厂的转型。该方法使用附加的三轴传感器来辅助机器监控。使用神经网络分析获得的数据是否异常。实验结果证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New Method for Industry 4.0 Machine Status Prediction - A Case Study with the Machine of a Spring Factory
In response to the technological development in recent years, many technology giants are making efforts toward Industry 4.0. However, many small-and medium-sized factories cannot even computerize and automate their factories, which is the foundation of Industry 4.0, due to inadequate capital and scale. This is because the majority of these factories are still using conventional machines in which the data cannot be digitized. Consequently, they cannot achieve the goal of Industry 4.0. This work therefore proposes a simple approach that facilitates the transition of these small-and medium-sized factories. The approach uses add-on triaxial sensors to aid in machine monitoring. The data obtained is analyzed for abnormalities using neural networks. Experiment results demonstrate the validity of the proposed approach.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信