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Due to the amplitude features of the meshing frequency and the side frequency useless to distinguish the normal status and the slight wear fault, three shock features are extracted in this paper. One of the features called viscous damping ratio is extracted by matching the Laplace wavelet with the acquired signals, while another two features are the kurtosis values in the time-domain and the frequency-domain. Since the traditional matching method of Laplace wavelet is time-consumed and accuracy-low, a new method is proposed based on an advanced particle swarm optimization algorithm, where a probability model is adopted. Experiments on using the three proposed features to recognize the wear fault of gear in a farm tractor show the proposed features obtain 0.12 lower in error rate than the amplitude features of the meshing frequency and the side frequency.