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Context Awareness in Predictive Maintenance
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
2016 (English)In: Current Trends in Reliability, Availability, Maintainability and Safety, Springer, 2016, p. 197-216Chapter in book (Refereed)
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. p. 197-216
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-206101DOI: 10.1007/978-3-319-23597-4_15ISBN: 978-3-319-23596-7 (print)OAI: oai:DiVA.org:kth-206101DiVA, id: diva2:1091173
Funder
XPRES - Initiative for excellence in production research
Note

QC 20170519

Available from: 2017-04-26 Created: 2017-04-26 Last updated: 2017-05-19Bibliographically approved

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