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

作　　者：张苏祺 李浩[1] 张宇宁[1] 郑祥豪 丁海民[1,3] 李金伟[4] ZHANG Suqi;LI Hao;ZHANG Yuning;ZHENG Xianghao;DING Haimin;LI Jinwei(Key Laboratory of Power Station Energy Transfer Conversion and System,Ministry of Education,North China Electric Power University,Beijing 102206,China;College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108,China;Hebei Key Laboratory of Electric Machinery Health Maintenance&Failure Prevention,North China Electric Power University,Baoding 071003,China;China Institute of Water Resources and Hydropower Research,Beijing 100048,China)

机构地区：[1]华北电力大学电站能量传递转化与系统教育部重点实验室,北京102206 [2]福州大学电气工程与自动化学院,福州350108 [3]华北电力大学河北省电力机械装备健康维护与失效预防重点实验室,河北保定071003 [4]中国水利水电科学研究院,北京100048

出　　处：《水力发电学报》2023年第12期70-78,共9页Journal of Hydroelectric Engineering

基　　金：国家自然科学基金(51976056)。

摘　　要：开展水泵水轮机振动信号特征提取与智能识别研究对确保抽水蓄能电站的可靠安全运行具有重要意义。由于水泵水轮机运行工况复杂,激发机组振动的物理来源较多,振动信号所包含的频率成分较为复杂,传统方法难以准确提取复杂振动信号特征。针对此问题,本文提出了一种基于变分模态分解、气泡熵和长短时记忆神经网络的振动信号特征提取和智能识别模型。首先,利用变分模态分解对振动信号进行分析,得到若干模态;然后,计算各模态的气泡熵值,构建气泡熵特征向量;最后,采用长短时记忆神经网络对振动信号特征向量进行训练和识别。通过使用蒲石河抽水蓄能电站中水泵水轮机的顶盖复杂振动信号对本文所提模型进行验证,振动信号识别准确率可达到97.87%,表明其具有重要的工程应用价值。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 

分 类 号：TK734[交通运输工程—轮机工程]