Fault diagnosis in power plant based on multi-neural network

机译：基于多神经网络的电厂故障诊断

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摘要

Due to the complexity of the power plant production environment, it brings some difficulties to troubleshooting of turbine generator. Although the approach based on neural network has been widely used in fault diagnosis of equipment, the result of fault diagnosis, which is given by the single neural network, is often not ready to determine the fault type for turbine generator. In response to this situation, a fault diagnosis method based on multi-neural network is proposed on this paper. It means that the different neural network is to be used respectively for fault diagnosis of turbine vibration firstly. Then the results of these initial diagnoses are to be integrated with information fusion technology. Through this strategy, the reliable result of fault diagnosis is obtained and the disadvantage of inaccurate diagnosis based on a single neural network is overcome.

机译：由于电厂生产环境的复杂性，给涡轮发电机的故障排除带来了一些困难。尽管基于神经网络的方法已被广泛地用于设备的故障诊断中，但是由单个神经网络给出的故障诊断结果往往无法确定涡轮发电机的故障类型。针对这种情况，提出了一种基于多神经网络的故障诊断方法。这意味着首先将不同的神经网络分别用于涡轮振动的故障诊断。然后将这些初始诊断的结果与信息融合技术集成在一起。通过该策略，可获得可靠的故障诊断结果，克服了基于单个神经网络的诊断不准确的缺点。

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来源
《2014 IEEE International Conference on System Science ang Engineering》|2014年|180-184|共5页
会议地点 Shanghai(CN)
作者
Xia Fei; Zhang Hao; Peng Daogang;
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作者单位

Faculty of Automation Engineering, Shanghai University of Electric Power, China;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Artificial neural networks; Reliability; Turbines; Vibrations;

机译：人工神经网络;可靠性;涡轮机振动；;

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1. Framework for fault diagnosis with multi-source sensor nodes in nuclear power plants based on a Bayesian network [J] . Guohua Wu, Jiejuan Tong, Liguo Zhang, Annals of nuclear energy . 2018,第DECa期

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2. Advanced fault diagnosis method for nuclear power plant based on convolutional gated recurrent network and enhanced particle swarm optimization [J] . Wang Hang, Peng Min-jun, Ayodeji Abiodun, Annals of nuclear energy . 2021,第Feba期

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3. Research on intelligent fault diagnosis method for nuclear power plant based on correlation analysis and deep belief network [J] . Peng Bin-Sen, Xia Hong, Liu Yong-Kuo, Progress in Nuclear Energy . 2018,第sepa期

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4. Fault diagnosis in power plant based on multi-neural network [C] . Xia Fei, Zhang Hao, Peng Daogang IEEE International Conference on System Science ang Engineering . 2014

机译：基于多神经网络的电厂故障诊断
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机译：使用人工神经网络的核电站故障检测和诊断。
6. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants [O] . Paola Costamagna, Andrea De Giorgi, Alberto Gotelli, 2016

机译：基于SOFC的发电厂的故障诊断策略
7. The Fault Diagnosis using Two-Steps Neural Networks for Nuclear Power Plants [O] . Hyeon Bae, Soon-Il Kwon, Jong-Kyu Lee, 2002

机译：对核电站的两步神经网络的故障诊断
8. Nuclear power plant fault-diagnosis using artificial neural networks. [R] . Kim, K., Aljundi, T. L., Bartlett, E. B. 1992

机译：基于人工神经网络的核电站故障诊断。

Fault diagnosis in power plant based on multi-neural network

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