An Industrial Analytics Approach to Predictive Maintenance for Machinery Applications

被引：0
|
作者：
Gatica, Carlos Paiz [1 ]
Koester, Markus [1 ]
Gaukstern, Tobias [1 ]
Berlin, Eugen [2 ]
Meyer, Marek [2 ]
机构：
[1] Weidmuller Grp, Detmold, Germany
[2] AGT Int, Darmstadt, Germany
来源：
2016 IEEE 21ST INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA) | 2016年
关键词：
predictive maintenance; industrial analytics;
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：
A key challenge in nowadays machinery applications is to guaranty availability and to increase productivity while reducing production costs. Companies, which are able to use their produced data (e.g., process values) to overcome this challenge, will have a significant competitive advantage. This paper shows how data-analytics approaches can be used to realize predictive maintenance solutions, taking advantage of already existing data of production facilities, and reducing the realization effort. The main advantages of data-analytic approaches are explained by means of two industrial applications.
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页数：4
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