Feature extraction of gear tooth crack based on the method of surrogate data

Nonlinear characteristics always exist in mechanical systems, in particular, if faults occur in mechanical systems. Therefore the nonlinear component may be used to identify the faults. The primary goal of this paper was to exact the feature of gear tooth crack by means of the method of surrogate data from nonlinear dynamics using real vibration signal measured on a gear test device. Surrogate data were generated from a simpler linear model and had similar statistical characteristics with the original data. The mutual information was applied as the statistic to determine if the original data were significantly different from its surrogates. The vibration signal was divided into several sections. Each section´s failure may be identified through computing surrogates significant levels. The results showed that the different gear running states had different significant levels of surrogate data, and the changes of running states had the same trend with the increase process of gear crack. It was concluded that the significant level could be used as the numerical basis for fault diagnosis and gear case analysis, which was conducive to the further analyze the fault development.