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.
Reference:
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.
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
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
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.
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