融合EEMD-CNN的水电机组磨碰故障声纹识别模型OACSTPCD

Hydroelectric unit voice signals contain a significant amount of valuable information reflecting their internal mechanical state.To accurately extract the voiceprint features of rubbing faults in hydroelectric units,this paper presents a hydroelectric unit rubbing fault voiceprint recognition model based on the fusion of Ensemble Empirical Mode Decomposition(EEMD)and Convolutional Neural Network(CNN).First,we use EEMD to decompose a noise signal from a hydroelectric unit into several Intrinsic Mode Functions(IMFs)and a residue component(Res);we use these IMFs and Res,along with the original signal,to construct a fusion feature vector.Then,the vector is used as an input to train a CNN deep learning neural network,with the normal and rubbing fault test data as samples,so as to obtain a rubbing fault recognizer for hydroelectric units.This new method is validated against the rubbing test data from both the hydro-mechanical coupling test stand and the in-situ experiment,with an average accuracy of 99.8%,demonstrating its performance superior to other recognition models for the rubbing faults of hydroelectric units.