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Context Awareness in Predictive Maintenance
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0002-8906-630X
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Luleå University of Technology, Luleå, Sweden. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0002-4107-0991
University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science. KTH Royal Institute of Technology, Stockholm, Sweden. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-8679-8049
2016 (English)In: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective / [ed] Uday Kumar, Alireza Ahmadi, Ajit Kumar Verma & Prabhakar Varde, Springer, 2016, 197-211 p.Chapter in book (Refereed)Text
Abstract [en]

Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance approach utilizes the condition monitoring (CM) data to predict the future machine conditions and makes decisions upon this prediction. Recent development in CM leads to context aware approach where in parallel with CM measurements also data and information related to the context are gathered. Context could be operational condition, history of machine usage and performed maintenance actions. In general more obtained information gives better accuracy of prediction. It is important to track operational context in dynamically changing environment. Today in manufacturing we can observe shift from mass production to mass customisation. This leads to changes from long series of identical products to short series of different variants. Therefore implies changing operational conditions for manufacturing equipment. Moreover, where asset consist of multiple identical or similar equipment the context aware method can be used to combine in reliable way information. This should allow to increase accuracy of prediction for population as a whole as well as for each equipment instances. Same of those data have been already recorded and stored in industrial IT systems. However, it is distributed over different IT systems that are used by different functional units (e.g. maintenance department, production department, quality department, tooling department etc.). This paper is a conceptual paper based on initial research work and investigation in two manufacturing companies from automotive industry.

Place, publisher, year, edition, pages
Springer, 2016. 197-211 p.
Series
Lecture Notes in Mechanical Engineering, ISSN 2195-4356
Keyword [en]
Context modeling, Context awareness, Condition monitoring, Condition based maintenance, Predictive maintenance
National Category
Reliability and Maintenance
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-11826DOI: 10.1007/978-3-319-23597-4_15ISBN: 978-3-319-23597-4ISBN: 978-3-319-23596-7OAI: oai:DiVA.org:his-11826DiVA: diva2:893646
Available from: 2016-01-13 Created: 2016-01-13 Last updated: 2017-01-03Bibliographically approved

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Schmidt, BernardGalar, DiegoWang, Lihui
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