The timedomain dimensionless parameters that are sensitive to the fault models are used widely in mechanical fault diagnosis, but it also has serious crossover problems, that is, the dimensionless parameters have aliasing in the feature space for different fault states. In order to solve this problem, a method based on timedomain dimensionless parameters and Extreme Learning Machine(ELM) is presented, which is used for fault diagnosis of rolling bearings. The accuracy and effectiveness of the method was validated by the actual Rolling experimental data from a public site on the net. The experimental results show that further compared with the Back Propagation (BP)，State Vector Machine (SVM) and Grip search SVM for advantages，rolling bearing fault diagnosis method based on timedomain dimensionless parameters and ELM has faster speed and higher classification accuracy．