[1]许凯,富威,陈世均,等.基于LSTM神经网络的汽轮发电机状态监测系统[J].应用科技,2020,47(6):96-100.[doi:10.11991/yykj.202007003]
 XU Kai,FU Wei,CHEN Shijun,et al.State monitoring system of turbine generator based on LSTM neural network[J].Applied science and technology,2020,47(6):96-100.[doi:10.11991/yykj.202007003]
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基于LSTM神经网络的汽轮发电机状态监测系统(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第47卷
期数:
2020年6期
页码:
96-100
栏目:
机电工程
出版日期:
2021-01-31

文章信息/Info

Title:
State monitoring system of turbine generator based on LSTM neural network
作者:
许凯1 富威1 陈世均2 孟宇龙3 马佳瑞1
1. 哈尔滨工程大学 机电工程学院,黑龙江 哈尔滨 150001;
2. 中广核苏州热工研究院有限公司,江苏 苏州 215000;
3. 哈尔滨工程大学 计算机科学与技术学院,黑龙江 哈尔滨 150001
Author(s):
XU Kai1 FU Wei1 CHEN Shijun2 MENG Yulong3 MA Jiarui1
1. College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China;
2. Suzhou Nuclear Power Research Institute, China General Nuclear Power Corporation, Suzhou 215000, China;
3. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
关键词:
机器学习健康预测汽轮发电机数据处理预测性维护LSTM神经网络状态监测系统信号分析
Keywords:
machine learninghealth predictionturbo-generatordata processingpreventive maintenanceLSTM neural networkcondition monitoring systemsignal analysis
分类号:
TM623.3
DOI:
10.11991/yykj.202007003
文献标志码:
A
摘要:
为了快速获取汽轮发电机运行参数及其分析处理结果,完成运行状态的判断识别,本文以某核电厂使用的汽轮发电机状态参数为分析依据进行监测研究。先分别以机器学习算法中的线性回归和长短期记忆(LSTM)神经网络为数据处理方法,对2种方法得到的状态参数预测结果进行对比分析,确定以LSTM神经网络为状态监测核心算法,设计并搭建了汽轮发电机状态监测系统。之后采用传统的振动信号分析方法对汽轮发电机运行状态监测结果进行验证分析,保证监测结果准确可靠。实验结果表明:基于LSTM神经网络的状态监测方法能够有效处理运行参数,提取数据中隐含的设备状态信息,实现汽轮发电机状态实时监测,提高发电机组整体工作的稳定性与安全性,降低故障导致机组停机损毁的概率。
Abstract:
In order to quickly obtain the operating parameters of turbo-generator and the results of analysis and processing, and complete the judgment and identification of the operating state, the monitoring and research is carried out on the basis of the analysis of the turbo-generator state parameters used in a nuclear power plant. Firstly, the linear regression and long short term memory (LSTM) neural network in machine learning algorithm are used as data processing methods, and the prediction results of state parameters obtained by the two methods are compared and analyzed. Based on the LSTM neural network, the condition monitoring system of turbo-generator is designed and built. Finally, the traditional vibration signal analysis method is used to verify and analyze the monitoring results of the running state of turbo-generator, ensuring the accuracy and reliability of the monitoring results. The results showed that the condition monitoring method based on LSTM neural network can deal with the operation parameters effectively, extract the equipment state information implied in the data, realize the real-time monitoring of the turbo-generator condition, improve the stability and safety of the whole operation of the generator set, and reduce the probability of the unit shutdown damage caused by the failure.

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备注/Memo

备注/Memo:
收稿日期:2020-07-03。
作者简介:许凯,男,硕士研究生;富威,男,副教授,博士
通讯作者:富威,E-mail:fuwei@hrbeu.edu.cn
更新日期/Last Update: 2021-02-05