Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89145
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dc.contributor.authorHong, Weien
dc.contributor.authorCai, Wenjianen
dc.contributor.authorWang, Shaopingen
dc.contributor.authorTomovic, Mileta M.en
dc.date.accessioned2018-05-17T04:24:36Zen
dc.date.accessioned2019-12-06T17:18:52Z-
dc.date.available2018-05-17T04:24:36Zen
dc.date.available2019-12-06T17:18:52Z-
dc.date.issued2017en
dc.identifier.citationHong, W., Cai, W., Wang, S., & Tomovic, M. M. (2018). Mechanical wear debris feature, detection, and diagnosis: A review. Chinese Journal of Aeronautics, 31(5), 867-882.en
dc.identifier.issn1000-9361en
dc.identifier.urihttps://hdl.handle.net/10356/89145-
dc.description.abstractMechanical debris is an important product of friction wear, which is also a crucial approach to know the running status of a machine. Many studies have been conducted on mechanical debris in related fields such as tribology, instrument, and diagnosis. This paper presents a comprehensive review of these studies, which summarizes wear mechanisms (e.g., abrasive wear, fatigue wear, and adhesive wear) and debris features (e.g., concentration (number), size, morphology, and composition), analyzes detection methods principles (e.g., offline: spectrograph and ferrograph, and online: optical method, inductive method, resistive-capacitive method, and acoustic method), reviews developments of online inductive methods, and investigates the progress of debris-based diagnosis. Finally, several notable problems are discussed for further studies.en
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en
dc.format.extent16 p.en
dc.language.isoenen
dc.relation.ispartofseriesChinese Journal of Aeronauticsen
dc.rights© 2017 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en
dc.subjectDebris Detectionen
dc.subjectDebris Featureen
dc.titleMechanical wear debris feature, detection, and diagnosis: A reviewen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.identifier.doi10.1016/j.cja.2017.11.016en
dc.description.versionPublished versionen
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