Bearings are one of the most widely used components in the industry that are more vulnerable than other parts of machines. In this research, a precise method was developed for diagnosis bearing detection based on vibrating signals. Vibration signals were recorded from four common faults in the bearings at three speeds of 1800, 3900, and 6600 rpm. The vibration signals were transmitted by the fast Fourier transform to the frequency domain. A total of 24 features were extracted from frequency and time signals. The superior features are selected using the combination of genetic algorithm and artificial neural network. A support vector machine is used to intelligently detect ball bearing faults. The accuracy of the support vector machine with all extracted features in different revolutions showed that the highest accuracy for training and test data was obtained 78.86% and 69.33% respectively, at 1800 rpm. The results of reduction and selection of superior features showed that the highest accuracy of the support machine was obtained in the classification of ball bearing faults for training and test data 97.14% and 93.33%, respectively. The results show that the use of the feature selection method based on the genetic algorithm will increase the accuracy of the classification.
