Semantic Framework for Predictive Maintenance in a Cloud Environment
Autor: | Lihui Wang, Diego Galar, Bernard Schmidt |
---|---|
Jazyk: | angličtina |
Rok vydání: | 2017 |
Předmět: |
0209 industrial biotechnology
Engineering Decision support system media_common.quotation_subject Knowledge management Cloud computing 02 engineering and technology 010501 environmental sciences Electrical Engineering Electronic Engineering Information Engineering 01 natural sciences Predictive maintenance 020901 industrial engineering & automation Quality (business) Product (category theory) Cloud manufacturing Elektroteknik och elektronik Productivity 0105 earth and related environmental sciences General Environmental Science media_common business.industry Semantic framework Reliability engineering Risk analysis (engineering) General Earth and Planetary Sciences business |
Popis: | Proper maintenance of manufacturing equipment is crucial to ensure productivity and product quality. To improve maintenance decision support, and enable prediction-as-a-service there is a need to provide the context required to differentiate between process and machine degradation. Correlating machine conditions with process and inspection data involves data integration of different types such as condition monitoring, inspection and process data. Moreover, data from a variety of sources can appear in different formats and with different sampling rates. This paper highlights those challenges and presents a semantic framework for data collection, synthesis and knowledge sharing in a Cloud environment for predictive maintenance. CC BY-NC-ND 4.0Edited by Roberto Teti |
Databáze: | OpenAIRE |
Externí odkaz: |