Abstract: Aiming at the problem that the traditional mechanical fault diagnosis technology based on vibration signal was too complex and the diagnosis time was long, a fast identification method of gearbox bearing and gear fault based on K-means and Gaussian mixture model clustering method was proposed. First, the vibration signal was decomposed by empirical mode decomposition (EMD), and the IMF component which best expressed the local characteristics of the vibration signal was selected by correlation analysis. The vibration signal feature set was constituted by the root mean square values of the IMF component and the original vibration signal. Then, the classifiable number of the vibration signal feature set was determined by using K-means algorithm. Finally, based on the vibration signal feature set and its classifiable number, the multi-dimensional Gaussian distribution function of gearbox operation state was constructed by using Gaussian mixture model clustering, and the dependent probability model of gearbox was established in each running state. According to the value of subordinate probability, the gearbox fault could be identified quickly.The experimental and research results show that the average accuracy of the gearbox fault identification method based on K-means and Gaussian mixture model clustering is 94.3%, which is higher than the gearbox fault identification method based on fuzzy c-means clustering(84.5%)in the experimental environment.

Key words:  gearbox; K-means clustering;Gaussian mixture model based clustering; fault identification