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Industry 4.0 in maintenance: Using condition monitoring in electric machines

2022-01-24Conference object Open access
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dc.contributor.authorDomingues, Nuno
dc.date.accessioned2022-01-31T16:29:53Z
dc.date.available2022-01-31T16:29:53Z
dc.date.issued2022-01-24
dc.description.abstractIndustry traditionally considered maintenance as a cost and a necessity to replace equipment and machines, but the path has changed to better focus on maintenance to prevent faults and it was designated as predictive. The ones motivated to take these advantages are faced with two of the biggest barriers: the investment it requires and the difficulty to develop algorithms. The costs of installation are still high, but the avoided costs surpass it. Also, Internet of Things (IoT) has brought a big shift, which is been known as the industry 4.0. One of the potentials in maintenance is the conditioning monitoring. Condition monitoring sensors and devices are now linked to maintenance platforms, providing real-time data. This new connectivity is both more affordable and easier to implement than predictive maintenance. Real-time data allows managers to adjust preventive maintenance plans while providing greater reliability. At the same time, artificial intelligence manages this data to recognize patterns, which is one of the most promising advances in digital reliability. So, regardless of the ability to immediately implement a preventive maintenance plan, condition monitoring is an asset itself. The present paper presents the common faults on electric machines, their effects, their impact on the industry and the main techniques on condition control to prevent them. It is also added the reflection on the use of IoT to enhance the potential of condition control maintenance. The implementation of continuous improvement actions throughout the life of the equipment allows to increase efficiency, either by overcoming weaknesses or by adapting production or operational capacities to processes, production or maintenance, avoiding under maintenance or over maintenance and minimizing operating costs.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationDOMINGUES, Nuno – Industry 4.0 in maintenance: Using condition monitoring in electric machines. In 2021 International Conference on Decision Aid Sciences and Application (DASA). Sakheer, Bahrain: IEEE, 2022. ISBN 978-1-6654-1634-4. Pp. 1-7.pt_PT
dc.identifier.doi10.1109/DASA53625.2021.9682254pt_PT
dc.identifier.isbn978-1-6654-1634-4
dc.identifier.isbn978-1-6654-1635-1
dc.identifier.urihttp://hdl.handle.net/10400.21/14230
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9682254&tag=1pt_PT
dc.subjectCondition monitoringpt_PT
dc.subjectCondition controlpt_PT
dc.subjectDamage detectionpt_PT
dc.subjectVibration analysispt_PT
dc.subjectModel-updatingpt_PT
dc.titleIndustry 4.0 in maintenance: Using condition monitoring in electric machinespt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlace7-8 Dec. 2021 - Sakheer, Bahrainpt_PT
oaire.citation.endPage7pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2021 International Conference on Decision Aid Sciences and Application (DASA)pt_PT
person.familyNameSoares Domingues
person.givenNameNuno Alexandre
person.identifier.ciencia-idB416-C3B2-2CBF
person.identifier.orcid0000-0003-0763-8106
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationd9437ee3-91ad-4034-91f1-4dd3f097c16c
relation.isAuthorOfPublication.latestForDiscoveryd9437ee3-91ad-4034-91f1-4dd3f097c16c

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