Classification for Fault of Generator Rotor Based on Hilbert Time-frequency Spectrum and Neural Network
Yun Zhang · Machine Tool & Hydraulics · 2006
A new fault detection method for signal classification in generator was presented.It is a new method for processing non-stationary signal.This method decomposes the one-dimensional signals into intrinsic mode functions(IMFs) using empirical mode decomposition method and then calculates the meaningful multi-component instantaneous frequency.Applied to a fault signals analysis,it can provide more new time-frequency attributes.Then the moments,margins and entropy of the time-frequency spectrum can be calculated as the feature vectors.The probabilistic neural network can be used to classify different fault modes.The accuracy and robustness of the proposed methods were investigated on signals of different fault condition in generator rotor.