Intelligent machine fault detection using SOM based RBF neural networks

A radial-basis-function (RBF) neural network based fault detection system is developed for performing induction machine fault detection and analysis. The optimal network architecture of the RBF network is determined automatically by our proposed cell-splitting, grid (CSG) algorithm. This facilitates the conventional laborious trial-and-error procedure in establishing an optimal architecture. The proposed RBF machine fault diagnostic system has been intensively tested with unbalanced electrical faults and mechanical faults operating at different rotating speeds. The proposed system is not only able to detect electrical and mechanical faults, but the system is also able to estimate the extent of faults.