A pre-processing methodology to enhance novel information for rotating machine diagnostics

dc.contributor.authorSchmidt, Stephan
dc.contributor.authorHeyns, P.S. (Philippus Stephanus)
dc.contributor.authorGryllias, Konstantinos C.
dc.date.accessioned2019-03-05T11:45:35Z
dc.date.issued2019-06
dc.description.abstractMany sophisticated signal analysis techniques are developed to efficiently detect, localise and trend damage in rotating machine components such as bearings and gears for example. However, these techniques are generally applied without effectively incorporating historical information when performing condition monitoring. It is possible to enhance the performance of the analysis techniques by incorporating historical data from a machine in a reference condition. In this paper, a methodology is proposed to extract a novel signal i.e. a signal that contains information that is not present in the historical reference data, from a vibration signal. This is performed by utilising the available historical data. Sophisticated signal analysis techniques can subsequently be used on the novel vibration signal to diagnose the machine. The benefits of the methodology are illustrated on data, generated from phenomenological gearbox model data and experimental gearbox data, by utilising advanced techniques based on cyclostationary analysis. The results indicate that the novel vibration signal is more sensitive to damage, which highlights its potential as a pre-processing technique for rotating machine applications where historical data are available.en_ZA
dc.description.departmentMechanical and Aeronautical Engineeringen_ZA
dc.description.embargo2020-06-01
dc.description.librarianhj2019en_ZA
dc.description.sponsorshipK.C. Gryllias gratefully acknowledges the Research Fund KU Leuven.en_ZA
dc.description.urihttp://www.elsevier.com/locate/jnlabr/ymsspen_ZA
dc.identifier.citationSchmidt, S., Heyns, P.S. & Gryllias, K.C. 2019, 'A pre-processing methodology to enhance novel information for rotating machine diagnostics', Mechanical Systems and Signal Processing, vol. 124, pp. 541-561.en_ZA
dc.identifier.issn0888-3270 (print)
dc.identifier.issn1096-1216 (online)
dc.identifier.other10.1016/j.ymssp.2019.02.005
dc.identifier.urihttp://hdl.handle.net/2263/68566
dc.language.isoenen_ZA
dc.publisherElsevieren_ZA
dc.rights© 2018 Published by Elsevier Ltd. Notice : this is the author’s version of a work that was accepted for publication in Mechanical Systems and Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Mechanical Systems and Signal Processing, vol. 124, pp. 541-561, 2018, doi : 10.1016/j.ymssp.2019.02.005.en_ZA
dc.subjectGearbox diagnosticsen_ZA
dc.subjectNovel information enhancementen_ZA
dc.subjectHistorical reference dataen_ZA
dc.subjectCyclostationary analysisen_ZA
dc.subjectVibration analysisen_ZA
dc.subjectVibration signalen_ZA
dc.subjectRotating machineen_ZA
dc.subjectReference dataen_ZA
dc.subjectReference conditionen_ZA
dc.subjectAnalysis techniquesen_ZA
dc.subjectSignal analysisen_ZA
dc.subjectShafts (machine components)en_ZA
dc.subjectRotating machineryen_ZA
dc.subjectGearsen_ZA
dc.subjectCondition monitoringen_ZA
dc.subjectBearings (machine parts)en_ZA
dc.subject.otherEngineering, built environment and information technology articles SDG-04
dc.subject.otherSDG-04: Quality education
dc.subject.otherEngineering, built environment and information technology articles SDG-08
dc.subject.otherSDG-08: Decent work and economic growth
dc.subject.otherEngineering, built environment and information technology articles SDG-09
dc.subject.otherSDG-09: Industry, innovation and infrastructure
dc.subject.otherEngineering, built environment and information technology articles SDG-12
dc.subject.otherSDG-12: Responsible consumption and production
dc.subject.otherEngineering, built environment and information technology articles SDG-13
dc.subject.otherSDG-13: Climate action
dc.titleA pre-processing methodology to enhance novel information for rotating machine diagnosticsen_ZA
dc.typePostprint Articleen_ZA

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