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Gear is commonly applied as a vital component that connects and transmits power, which plays a crucial role in mechanical system. In order to represent the running states of the gear effectively and improve the diagnosis accuracy further, a feature fusion strategy based on fusion assessment to obtain optimum feature fusion pattern is proposed in this research, including two-direction feature fusion strategy and three-direction feature fusion strategy. Firstly, different feature extraction methods are employed to extract the features of vibration signal in each direction (X, Y and Z axis), respectively. Then, the best fusion mode is determined by fusion assessment mechanism based on fuzzy logic in two directions and vibration intensity of signal in three directions. Finally, support vector machine (SVM) and decision tree (DT) are selected to verify the validity and universality of the proposed method. Experimental studies show that feature fusion strategy for gear states diagnosis based on fusion assessment can take full advantage of the complementary performances of different feature extraction methods and signal characteristics in different directions, which can fully represent the health states of the running gear and effectively improve the diagnosis accuracy.

Research on feature fusion strategy for gear states diagnosis based on fusion assessment