Vibration signals generated from gears often exhibit nonlinearity. Characterization of such signals using nonlinear time series analysis can be a good alternative for identifying gear faults. This paper presets a recurrence network based approach to extract features from vibration signals for gear fault diagnosis. Quantitative parameters (such as mean degree centrality, global clustering coefficient, assortativity of the recurrence network, or network entropy) related to the dynamical complexity of the vibration signals are calculated from the generated recurrence network to help classify different gear faults with two kinds of classifiers, i.e., support vector machine and extreme learning machine. Experimental studies performed on two different gear test systems have verified the effectiveness of the presented recurrence network approach for gear fault severity evaluation, as well as gear fault classification.
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