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Diagnosis of machine tools: Assessment based on double ball-bar measurements from a population of similar machines
KTH, School of Industrial Engineering and Management (ITM), Production Engineering. School of Engineering Science, University of Skövde, PO Box 408, Skövde, 541 28, Sweden.ORCID iD: 0000-0001-8679-8049
2018 (English)In: 51st CIRP Conference on Manufacturing Systems, Elsevier, 2018, Vol. 72, p. 1327-1332Conference paper, Published paper (Refereed)
Abstract [en]

The presented work is toward population-based predictive maintenance of manufacturing equipment with consideration of the automatic selection of signals and processing methods. This paper describes an analysis performed on double ball-bar measurement from a population of similar machine tools. The analysis is performed after aggregation of information from Computerised Maintenance Management System, Supervisory Control and Data Acquisition, NC-code and Condition Monitoring from a time span of 4 years. Economic evaluation is performed with use of Monte Carlo simulation based on data from real manufacturing setup.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 72, p. 1327-1332
Series
Procedia CIRP, ISSN 2212-8271 ; 72
Keywords [en]
Automatic signal selection, Condition monitoring, Population based maintenance
National Category
Reliability and Maintenance
Identifiers
URN: urn:nbn:se:kth:diva-238415DOI: 10.1016/j.procir.2018.03.208Scopus ID: 2-s2.0-85049577336OAI: oai:DiVA.org:kth-238415DiVA, id: diva2:1262478
Conference
51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018, Stockholm Waterfront Congress CentreStockholm, Sweden, 16 May 2018 through 18 May 2018
Note

QC 20181112

Available from: 2018-11-12 Created: 2018-11-12 Last updated: 2018-11-12Bibliographically approved

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Wang, Lihui

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