Maintenance 4.0: Intelligent and Predictive Maintenance System Architecture

被引：0

作者：

Cachada, Ana ^{[1
]}

Barbosa, Jose ^{[1
]}

Leitao, Paulo ^{[1
]}

Geraldes, Carla A. S. ^{[2
,3
]}

Deusdado, Leonel ^{[2
]}

Costa, Jacinta ^{[2
]}

Teixeira, Carlos ^{[4
]}

Teixeira, Joao ^{[4
]}

Moreira, Antonio H. J. ^{[5
]}

Moreira, Pedro Miguel ^{[6
]}

Romero, Luis ^{[6
]}

机构：

[1] Inst Politecn Braganca, Res Ctr Digitalizat & Intelligent Robot CeDRI, Campus Santa Apolonia, P-5300253 Braganca, Portugal

[2] Polytech Inst Braganca, Campus Sta Apolonia, P-5300253 Braganca, Portugal

[3] Univ Minho, Ctr ALGORITMI, Campus Azurem, P-4800058 Guimaraes, Portugal

[4] Catraport Lda, Zona Ind Mos, Lote 1, P-5300692 Braganca, Portugal

[5] 2Ai Polytech Inst Cavado & Ave, Barcelos, Portugal

[6] Inst Politecn Viana do Castelo, ARC4DigiT Appl Res Ctr Digital Transformat, Av Atlantico, P-4900348 Viana Do Castelo, Portugal

来源：

2018 IEEE 23RD INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA) | 2018年

关键词：

Industry; 4.0; industrial maintenance; predictive maintenance; data analysis; augmented reality;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In the current manufacturing world, the role of maintenance has been receiving increasingly more attention while companies understand that maintenance, when well performed, can be a strategic factor to achieve the corporate goals. The latest trends of maintenance leans towards the predictive approach, exemplified by the Prognosis and Health Management (PHM) and the Condition-based Maintenance (CBM) techniques. The implementation of such approaches demands a well structured architecture and can be boosted through the use of emergent ICT technologies, namely Internet of Things (IoT), cloud computing, advanced data analytics and augmented reality. Therefore, this paper describes the architecture of an intelligent and predictive maintenance system, aligned with Industry 4.0 principles, that considers advanced and online analysis of the collected data for the earlier detection of the occurrence of possible machine failures, and supports technicians during the maintenance interventions by providing a guided intelligent decision support.

引用

页码：139 / 146

页数：8

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