Maintenance Analytics - The New Know in Maintenance

被引：43

作者：

Karim, Ramin ^{[1
]}

Westerberg, Jesper ^{[2
]}

Galar, Diego ^{[1
]}

Kumar, Uday ^{[1
]}

机构：

[1] Lulea Univ Technol, Div Operat, Maintenance Engn, SE-97187 Lulea, Sweden

[2] eMaintenance365 AB, Aurorum 1C, SE-97775 Lulea, Sweden

来源：

IFAC PAPERSONLINE | 2016年 / 49卷 / 28期

关键词：

big data; maintenance analytics; eMaintenance; Knowledge discovery; maintenance decision support; KNOWLEDGE DISCOVERY; DECISION-SUPPORT;

D O I：

10.1016/j.ifacol.2016.11.037

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Decision-making in maintenance has to be augmented to instantly understand and efficiently act, i.e. the new know. The new know in maintenance needs to focus on two aspects of knowing: 1) what can be known and 2) what must be known, in order to enable the maintenance decision-makers to take appropriate actions. Hence, the purpose of this paper is to propose a concept for knowledge discovery in maintenance with focus on Big Data and analytics. The concept is called Maintenance Analytics (MA). MA focuses in the new knowledge discovery in maintenance. MA addresses the process of discovery, understanding, and communication of maintenance data from four time-related perspectives, i.e. 1) Maintenance Descriptive Analytics (monitoring); 2) Maintenance Diagnostic Analytics; 3) Maintenance Predictive Analytics; and 4) Maintenance Prescriptive analytics. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：214 / 219

页数：6

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