水力发电学报2023，Vol.42Issue(12)：70-78,9.DOI:10.11660/slfdxb.20231207
水泵水轮机复杂振动信号特征提取与智能识别
Feature extraction and intelligent recognition of complicated vibration signals of pump turbine
摘要
Abstract
Feature extraction and intelligent recognition of the vibration signals of pump turbines are significant to reliable and safe operation of a pumped storage power station.Due to its complicated operational conditions,a pump turbine in operation can create a large number of physical sources that excite its vibrations,and the frequency components of the vibration signals are quite complicated.The traditional methods suffer a poor accuracy of feature extraction from a complicated vibration signal.To improve the accuracy,this paper describes a new model of feature extraction and intelligent recognition of the vibration signals,based on the variational mode decomposition(VMD),bubble entropy(BE),and long short-term memory(LSTM)neural network.First,this method analyzes the vibration signal using VMD and obtains several modes.Then for each mode,its BE value is calculated and a BE eigenvector is constructed.Finally,the eigenvectors of the vibration signal are trained and recognized using a LSTM neural network.We have verified the method against the complicated vibration signals measured at the top cover of a pump turbine at the Pushihe pumped storage station,and achieved a signal recognition accuracy of 97.87%,indicating its important engineering application value.关键词
水泵水轮机/振动信号/变分模态分解/气泡熵/长短时记忆神经网络Key words
pump turbine/vibration signal/variational mode decomposition/bubble entropy/long short-term memory分类
能源科技引用本文复制引用
张苏祺,李浩,张宇宁,郑祥豪,丁海民,李金伟..水泵水轮机复杂振动信号特征提取与智能识别[J].水力发电学报,2023,42(12):70-78,9.基金项目
国家自然科学基金(51976056) （51976056）
