Intelligent Fault Diagnosis (IFD) refers to the application of machine learning theories to machine fault diagnosis and seems to be a promising way to releases the contribution from human labor, since it automatically recognizes the state of health of machine. The work presented in the thesis has as objective the identification, training, application and evaluation of an artificial intelligence algorithm suitable for assessing the state of health of a spherical roller bearing rotating system through the analysis of its vibration path. The first step consists in the creation, using the approach of nonlinear multi-body models, of the dynamic model of a real bearing, which can simulate its behavior in presence of localized defects on the inner or outer raceway.