Research on Mechanical Mechanics with Fault Diagnosis Method for Gearbox Based on Relevance Vector Machine

In order to more effectively solve some difficult problems in gearbox failure diagnosis, a gearbox fault diagnosis method based on Relevance Vector Machine (RVM) is proposed. RVM is developed in Bayesian framework. It does not need to estimate the regularization parameter with less relevance vectors, and its kernel function does not need to satisfy Mercer condition. Simulation results show that: compared with the traditional BP neural network, RVM has the faster modeling speed, more accurate diagnosis, and is worthy of promotion and application in fault diagnosis of the gearbox.