Fault diagnosis and condition monitoring of rotating machines are important for the maintenance of the gas turbine system. In this paper, the Lab-scale rotor test device is simulated by a gas turbine, and faults are simulated such as Rubbing, Misalignment and Unbalance, which occurred from a gas turbine critical fault mode. In addition, blade rubbing is one of the gas turbine main faults, as well as a hard to detect fault early using FFT analysis and orbit plot. However, through a feature based analysis, the fault classification is evaluated according to several critical faults. Therefore, the possibility of a feature analysis of the vibration signal is confirmed for rotating machinery. The fault simulator for an acquired vibration signal is a rotor-kit based test rig with a simulated blade rubbing fault mode test device. Feature selection based on GA (Genetic Algorithms) one of the feature selection algorithm is selected. Then, through the Support Vector Machine, one of machine learning, feature classification is evaluated. The results of the performance of the GA compared with the PCA (Principle Component Analysis) for reducing dimension are presented. Therefore, through data learning, several main faults of the gas turbine are evaluated by fault classification using the SVM (Support Vector Machine).