In order to overcome some inherent defects of Support Vector Machine(SVM), such as poor sparsity, heavy computation and kernel function satisfactory to the Mercer's conditions in the engine oil wear particle detection and the fault diagnosis, a new attempt by adopting a predictable method based on Relevance Vector Machine (RVM) is proposed. On the basis of the introduction of principle and deduction, the spectrum analysis data of a certain aero engine lubricating oil are utilized to predict the relationships between the aero engine oil wear particle concentration and fault. Through analysis and verification, the results show that the method based on RVM has more advantages in generalization over the SVMs and the ANNs under the same conditions, and the method can be widely used in the engine oil wear particle analysis and the failure prediction.