Please use this identifier to cite or link to this item: https://doi.org/10.3390/APP10186360
DC FieldValue
dc.titleAn open source framework approach to support condition monitoring and maintenance
dc.contributor.authorCampos, J.
dc.contributor.authorSharma, P.
dc.contributor.authorAlbano, M.
dc.contributor.authorFerreira, L.L.
dc.contributor.authorLarrañaga, M.
dc.date.accessioned2021-08-18T02:53:18Z
dc.date.available2021-08-18T02:53:18Z
dc.date.issued2020
dc.identifier.citationCampos, J., Sharma, P., Albano, M., Ferreira, L.L., Larrañaga, M. (2020). An open source framework approach to support condition monitoring and maintenance. Applied Sciences (Switzerland) 10 (18) : 6360. ScholarBank@NUS Repository. https://doi.org/10.3390/APP10186360
dc.identifier.issn20763417
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/197514
dc.description.abstractThis paper discusses the integration of emergent ICTs, such as the Internet of Things (IoT), the Arrowhead Framework, and the best practices from the area of condition monitoring and maintenance. These technologies are applied, for instance, for roller element bearing fault diagnostics and analysis by simulating faults. The authors first undertook the leading industry standards for condition-based maintenance (CBM), i.e., open system architecture-condition-based maintenance (OSA-CBM) and Machinery Information Management Open System Alliance (MIMOSA), which has been working towards standardizing the integration and interchangeability between systems. In addition, this paper highlights the predictive health monitoring methods that are needed for an effective CBM approach. The monitoring of industrial machines is discussed as well as the necessary details are provided regarding a demonstrator built on a metal sheet bending machine of the Greenbender family. Lastly, the authors discuss the benefits of the integration of the developed prototypes into a service-oriented platform, namely the Arrowhead Framework, which can be instrumental for the remotization of maintenance activities, such as the analysis of various equipment that are geographically distributed, to push forward the grand vision of the servitization of predictive health monitoring methods for large-scale interoperability. © 2020 by the authors.
dc.publisherMDPI AG
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2020
dc.subjectArrowhead framework
dc.subjectOSA-CBM
dc.subjectRolling element bearing fault
dc.subjectThe internet of things
dc.typeArticle
dc.contributor.departmentINDUSTRIAL SYSTEMS ENGINEERING AND MANAGEMENT
dc.description.doi10.3390/APP10186360
dc.description.sourcetitleApplied Sciences (Switzerland)
dc.description.volume10
dc.description.issue18
dc.description.page6360
Appears in Collections:Staff Publications
Elements

Show simple item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_3390_APP10186360.pdf3.6 MBAdobe PDF

OPEN

NoneView/Download

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons