水力发电学报2024，Vol.43Issue(1)：59-69,11.DOI:10.11660/slfdxb.20240106
融合EEMD-CNN的水电机组磨碰故障声纹识别模型
Voiceprint recognition model of hydropower unit rub-impact faults based on integrated EEMD-CNN
摘要
Abstract
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.关键词
水电机组/声纹信号/卷积神经网络/EEMD/故障诊断Key words
hydroelectric unit/voice signals/convolutional neural network/EEMD/fault diagnosis分类
能源与动力引用本文复制引用
肖博屹,曾云,刀方,邹屹东,李想,拜树芳..融合EEMD-CNN的水电机组磨碰故障声纹识别模型[J].水力发电学报,2024,43(1):59-69,11.基金项目
国家自然科学基金(52079059 （）
52269020) （）
