dc.contributor.author | Hidle, Einar Løvli | |
dc.contributor.author | Hestmo, Rune Harald | |
dc.contributor.author | Adsen, Ove Sagen | |
dc.contributor.author | Lange, Hans Iver | |
dc.contributor.author | Vinogradov, Alexei | |
dc.date.accessioned | 2022-11-28T09:47:32Z | |
dc.date.available | 2022-11-28T09:47:32Z | |
dc.date.created | 2022-10-19T12:58:18Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Sensors 2022, 22(14), 5187; https://doi.org/10.3390/s22145187 | en_US |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | https://hdl.handle.net/11250/3034385 | |
dc.description.abstract | Aiming at early detection of subsurface cracks induced by contact fatigue in rotating machinery, the knowledge-based data analysis algorithm is proposed for health condition monitoring through the analysis of acoustic emission (AE) time series. A robust fault detector is proposed, and its effectiveness was demonstrated for the long-term durability test of a roller made of case-hardened steel. The reliability of subsurface crack detection was proven using independent ultrasonic inspections carried out periodically during the test. Subsurface cracks as small as 0.5 mm were identified, and their steady growth was tracked by the proposed AE technique. Challenges and perspectives of the proposed methodology are unveiled and discussed. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.subject | subsurface crack | en_US |
dc.subject | rolling contact fatigue | en_US |
dc.subject | data processing | en_US |
dc.subject | acoustic emission | en_US |
dc.subject | fault diagnostics | en_US |
dc.title | Early Detection of Subsurface Fatigue Cracks in Rolling Element Bearings by the Knowledge-Based Analysis of Acoustic Emission | en_US |
dc.title.alternative | Early Detection of Subsurface Fatigue Cracks in Rolling Element Bearings by the Knowledge-Based Analysis of Acoustic Emission | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | Copyright: © 2022 by the authors. | en_US |
dc.source.pagenumber | 19 | en_US |
dc.source.volume | 22 | en_US |
dc.source.journal | Sensors | en_US |
dc.source.issue | 14 | en_US |
dc.identifier.doi | 10.3390/s22145187 | |
dc.identifier.cristin | 2062786 | |
dc.relation.project | Norges forskningsråd: 296236 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |