期刊
IFAC PAPERSONLINE
卷 49, 期 28, 页码 214-219出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ifacol.2016.11.037
关键词
big data; maintenance analytics; eMaintenance; Knowledge discovery; maintenance decision support
资金
- Swedish Rail Administration (Trafikverket)
- LKAB (the Swedish mining company)
- ePilot project
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.
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