A methodology using the spectral coherence and healthy historical data to perform gearbox fault diagnosis under varying operating conditions

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dc.contributor.author Schmidt, Stephan
dc.contributor.author Heyns, P.S. (Philippus Stephanus)
dc.contributor.author Gryllias, Konstantinos C.
dc.date.accessioned 2019-10-02T12:00:21Z
dc.date.available 2019-10-02T12:00:21Z
dc.date.issued 2020-01
dc.description.abstract Condition monitoring is usually performed over long periods of time when critical rotating machines such as wind turbine gearboxes are monitored. There are many potential signal processing and analysis techniques that can be utilised to diagnose the machine from the condition monitoring data, however, they seldom incorporate the available healthy historical data of a machine systematically in the fault diagnosis process. Hence, a methodology is proposed in this article which supplements the order-frequency spectral coherence with historical data from a healthy machine to perform automatic fault detection, automatic fault localisation and fault trending. This has the benefit that the order-frequency spectral coherence, a very powerful technique for rotating machine fault diagnosis under varying speed conditions, can be utilised without requiring an expert to interpret the results. In this methodology, an extended version of the improved envelope spectrum is utilised to extract features from the order-frequency spectral coherence, whereafter a probabilistic model is carefully used to calculate a diagnostic metric for automatic fault detection and localisation. The methodology is investigated on numerical gearbox data as well as experimental gearbox data, both acquired under time-varying operating conditions with two probabilistic models, namely a Gaussian model and a kernel density estimator, compared as well. The results indicate the potential of this methodology for performing gearbox fault diagnosis under varying operating conditions. en_ZA
dc.description.department Mechanical and Aeronautical Engineering en_ZA
dc.description.librarian hj2019 en_ZA
dc.description.sponsorship The Eskom Power Plant Engineering Institute (EPPEI) and the Research Fund KU Leuven. en_ZA
dc.description.uri http://www.elsevier.com/locate/apacoust en_ZA
dc.identifier.citation Schmidt S., Heyns P.S. & Gryllias K.C. 2020, 'A methodology using the spectral coherence and healthy historical data to perform gearbox fault diagnosis under varying operating conditions', Applied Acoustics, vol. 158, art. 107038, pp. 1-13. en_ZA
dc.identifier.issn 0003-682X
dc.identifier.other 10.1016/j.apacoust.2019.107038
dc.identifier.uri http://hdl.handle.net/2263/71555
dc.language.iso en en_ZA
dc.publisher Elsevier en_ZA
dc.rights © 2019 Elsevier Ltd. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Applied Acoustics. 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 Applied Acoustics, vol. 158, art. 107038, pp. 1-13, 2019. doi : 10.1016/j.apacoust.2019.107038. en_ZA
dc.subject Gearbox diagnostics en_ZA
dc.subject Novelty detection en_ZA
dc.subject Order-frequency spectral coherence en_ZA
dc.subject Time-varying operating conditions en_ZA
dc.title A methodology using the spectral coherence and healthy historical data to perform gearbox fault diagnosis under varying operating conditions en_ZA
dc.type Preprint Article en_ZA


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