第6部分总结:工业4.0中的预测性维护-方法,工具和可互操作的应用

G. Mentzas, K. Hribernik, K. Thoben, D. Kiritsis, A. Mousavi
{"title":"第6部分总结:工业4.0中的预测性维护-方法,工具和可互操作的应用","authors":"G. Mentzas, K. Hribernik, K. Thoben, D. Kiritsis, A. Mousavi","doi":"10.1002/9781119564034.PART6","DOIUrl":null,"url":null,"abstract":"Maintenance is a key operation function within manufacturing enterprises related to all of their processes and focuses not only on avoiding the equipment breakdown but also on improving business performance. In the last years, due to the evolution of technology, products and machines have become more and more complex. Consequently, the costs of time-based (planned) maintenance have increased and predictive maintenance has evolved as a novel lever for maintenance management. To this end, the emergence of the Internet of Things (IoT) can enhance the condition monitoring capabilities by paving the way for extensive use of physical and virtual sensors generating a multitude of data. In this way, predictive maintenance can significantly evolve in the frame of Industry 4.0. Industry 4.0 indicates the flexibility that exists in value-creating networks which enables machines and plants to adapt their behaviour to changing orders and operating conditions through self-optimization and reconfiguration with the aim to implement distributed and interconnected production facilities in future smart factories.","PeriodicalId":423825,"journal":{"name":"Enterprise Interoperability","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Part 6 Summary: Predictive Maintenance in Industry 4.0 - Methodologies, Tools and Interoperable Applications\",\"authors\":\"G. Mentzas, K. Hribernik, K. Thoben, D. Kiritsis, A. Mousavi\",\"doi\":\"10.1002/9781119564034.PART6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Maintenance is a key operation function within manufacturing enterprises related to all of their processes and focuses not only on avoiding the equipment breakdown but also on improving business performance. In the last years, due to the evolution of technology, products and machines have become more and more complex. Consequently, the costs of time-based (planned) maintenance have increased and predictive maintenance has evolved as a novel lever for maintenance management. To this end, the emergence of the Internet of Things (IoT) can enhance the condition monitoring capabilities by paving the way for extensive use of physical and virtual sensors generating a multitude of data. In this way, predictive maintenance can significantly evolve in the frame of Industry 4.0. Industry 4.0 indicates the flexibility that exists in value-creating networks which enables machines and plants to adapt their behaviour to changing orders and operating conditions through self-optimization and reconfiguration with the aim to implement distributed and interconnected production facilities in future smart factories.\",\"PeriodicalId\":423825,\"journal\":{\"name\":\"Enterprise Interoperability\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Enterprise Interoperability\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/9781119564034.PART6\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Enterprise Interoperability","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/9781119564034.PART6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

维修是制造企业的一项关键操作功能,涉及其所有流程,不仅关注避免设备故障,而且关注提高业务绩效。在过去的几年里,由于技术的发展,产品和机器变得越来越复杂。因此,基于时间(计划)的维护成本增加了,预测性维护已经发展成为维护管理的新杠杆。为此,物联网(IoT)的出现可以通过为广泛使用产生大量数据的物理和虚拟传感器铺平道路,从而增强状态监测能力。通过这种方式,预测性维护可以在工业4.0的框架中得到显著发展。工业4.0表明了价值创造网络中存在的灵活性,它使机器和工厂能够通过自我优化和重新配置来调整其行为以适应不断变化的订单和操作条件,目的是在未来的智能工厂中实现分布式和互联的生产设施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Part 6 Summary: Predictive Maintenance in Industry 4.0 - Methodologies, Tools and Interoperable Applications
Maintenance is a key operation function within manufacturing enterprises related to all of their processes and focuses not only on avoiding the equipment breakdown but also on improving business performance. In the last years, due to the evolution of technology, products and machines have become more and more complex. Consequently, the costs of time-based (planned) maintenance have increased and predictive maintenance has evolved as a novel lever for maintenance management. To this end, the emergence of the Internet of Things (IoT) can enhance the condition monitoring capabilities by paving the way for extensive use of physical and virtual sensors generating a multitude of data. In this way, predictive maintenance can significantly evolve in the frame of Industry 4.0. Industry 4.0 indicates the flexibility that exists in value-creating networks which enables machines and plants to adapt their behaviour to changing orders and operating conditions through self-optimization and reconfiguration with the aim to implement distributed and interconnected production facilities in future smart factories.
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