水力发电学报2026，Vol.45Issue(3)：82-92,11.DOI:10.11660/slfdxb.20260308
水轮机弱故障声学信号深度学习诊断研究
Deep learning-based acoustic diagnosis of weak faults in hydro turbines
摘要
Abstract
In the context of new power systems,wide-load operation of a hydro turbine has led to its increasingly complicated blade flow conditions and accelerated wear,making wear faults a major cause of its failure.From the acoustic perspective,this study develops a hydro turbine wear fault identification model based on the ensemble empirical mode decomposition with cumulative mean aggregation(EEMD)denoising,combined with a convolutional neural network(CNN)and a long-short-term memory network(LSTM).A raw signal is preprocessed by using EEMD to remove noise while retaining its key features.Then,we adaptively extract and reduce the dimensionality of the fault features by using CNN and input it into the LSTM model for feature learning and model training,achieving fault pattern identification.A hydro turbine fault test bench has been constructed for training and validation of the new EEMD-CNN-LSTM model.Experimental results demonstrate that this model is effective in wear fault identification,achieving an accuracy of 93.8%and outperforming the CNN,LSTM,or CNN-LSTM model.It improves accuracy by 6.7%,compared with the CNN-LSTM model without EEMD denoising.The findings are a valuable supplement to the previous studies of the monitoring and fault diagnosis systems of hydro turbines.关键词
水轮机/信号去噪/深度学习/故障诊断/声学Key words
hydro turbine/denoising/deep learning/fault diagnosis/acoustic分类
能源科技引用本文复制引用
李涵,刀方,曾云..水轮机弱故障声学信号深度学习诊断研究[J].水力发电学报,2026,45(3):82-92,11.基金项目
国家自然科学基金面上项目(52479084) （52479084）
国家自然科学基金青年科学基金项目(52509124) （52509124）
