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Combining synchronous averaging with a Gaussian mixture model novelty detection scheme for vibration-based condition monitoring of a gearbox

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dc.contributor.author Heyns, T
dc.contributor.author Heyns, PS
dc.contributor.author De Villiers, JP
dc.date.accessioned 2013-01-02T10:59:45Z
dc.date.available 2013-01-02T10:59:45Z
dc.date.issued 2012-10
dc.identifier.citation Heyns, T, De Villiers, J.P and Heyns, P.S. 2012. Combining synchronous averaging with a Gaussian mixture model novelty detection scheme for vibration-based condition monitoring of a gearbox. Mechanical Systems and Signal Processing, Vol. 32, pp 200-215. en_US
dc.identifier.issn 0888-3270
dc.identifier.uri http://www.sciencedirect.com/science/article/pii/S0888327012002221
dc.identifier.uri http://hdl.handle.net/10204/6408
dc.description Copyright: 2012 Elsevier. This is the Post-Print version of the work. The definitive version is published in Mechanical Systems and Signal Processing, Vol. 32, pp 200-215 en_US
dc.description.abstract This paper investigates how Gaussian mixture models (GMMs) may be used to detect and trend fault induced vibration signal irregularities, such as those which might be indicative of the onset of gear damage. The negative log likelihood (NLL) of signal segments are computed and used as measure of the extent to which a signal segment deviates from a reference density distribution which represents the healthy gearbox. The NLL discrepancy signal is subsequently synchronous averaged so that an intuitive, yet sensitive and robust, representation may be obtained which offers insight into the nature and extent to which a gear is damaged. The methodology is applicable to non-linear, non-stationary machine response signals. en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.relation.ispartofseries Workflow;9831
dc.subject Gaussian mixture models en_US
dc.subject GMMs en_US
dc.subject Gearbox monitoring en_US
dc.title Combining synchronous averaging with a Gaussian mixture model novelty detection scheme for vibration-based condition monitoring of a gearbox en_US
dc.type Article en_US
dc.identifier.apacitation Heyns, T., Heyns, P., & De Villiers, J. (2012). Combining synchronous averaging with a Gaussian mixture model novelty detection scheme for vibration-based condition monitoring of a gearbox. http://hdl.handle.net/10204/6408 en_ZA
dc.identifier.chicagocitation Heyns, T, PS Heyns, and JP De Villiers "Combining synchronous averaging with a Gaussian mixture model novelty detection scheme for vibration-based condition monitoring of a gearbox." (2012) http://hdl.handle.net/10204/6408 en_ZA
dc.identifier.vancouvercitation Heyns T, Heyns P, De Villiers J. Combining synchronous averaging with a Gaussian mixture model novelty detection scheme for vibration-based condition monitoring of a gearbox. 2012; http://hdl.handle.net/10204/6408. en_ZA
dc.identifier.ris TY - Article AU - Heyns, T AU - Heyns, PS AU - De Villiers, JP AB - This paper investigates how Gaussian mixture models (GMMs) may be used to detect and trend fault induced vibration signal irregularities, such as those which might be indicative of the onset of gear damage. The negative log likelihood (NLL) of signal segments are computed and used as measure of the extent to which a signal segment deviates from a reference density distribution which represents the healthy gearbox. The NLL discrepancy signal is subsequently synchronous averaged so that an intuitive, yet sensitive and robust, representation may be obtained which offers insight into the nature and extent to which a gear is damaged. The methodology is applicable to non-linear, non-stationary machine response signals. DA - 2012-10 DB - ResearchSpace DP - CSIR KW - Gaussian mixture models KW - GMMs KW - Gearbox monitoring LK - https://researchspace.csir.co.za PY - 2012 SM - 0888-3270 T1 - Combining synchronous averaging with a Gaussian mixture model novelty detection scheme for vibration-based condition monitoring of a gearbox TI - Combining synchronous averaging with a Gaussian mixture model novelty detection scheme for vibration-based condition monitoring of a gearbox UR - http://hdl.handle.net/10204/6408 ER - en_ZA


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