Cloud-enhanced predictive maintenance

Autor: Bernard Schmidt, Lihui Wang
Rok vydání: 2016
Předmět:
Zdroj: The International Journal of Advanced Manufacturing Technology. 99:5-13
ISSN: 1433-3015
0268-3768
DOI: 10.1007/s00170-016-8983-8
Popis: Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and makes decisions upon this prediction. The main aim of the present research is to achieve an improvement in predictive condition-based maintenance decision making through a cloud-based approach with usage of wide information content. For the improvement, it is crucial to identify and track not only condition related data but also context data. Context data allows better utilisation of condition monitoring data as well as analysis based on a machine population. The objective of this paper is to outline the first steps of a framework and methodology to handle and process maintenance, production, and factory related data from the first lifecycle phase to the operation and maintenance phase. Initial case study aims to validate the work in the context of real industrial applications. The RightsLink Digital Licensing and Rights Management Service (including RightsLink for Open Access) is available (A) to users of copyrighted works found at the websites of participating publishers who are seeking permissions or licenses to use those works, and (B) to authors of articles and other manuscripts who are seeking to pay author publication charges in connection with the submission of their works to publishers.
Databáze: OpenAIRE
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