Intelligent Condition Monitoring of Ball Bearings Faults by Combination of Genetic Algorithm and Support Vector Machines

摘要: Bearings are one of the most widely used components in industry that more vulnerable than other parts 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 bearings at three speeds 1800, 3900, and 6600 rpm. The vibration transmitted by fast Fourier transform to frequency domain. A total 24 features extracted time superior selected using combination genetic algorithm artificial neural network. support vector machine is intelligently detect ball faults. accuracy with all different revolutions showed highest training test data obtained 78.86% 69.33% respectively, 1800 rpm. results reduction selection classification 97.14% 93.33%, respectively. show use feature will increase classification.