{"title":"Fault diagnosis of cooling pipeline system","authors":"Peng Yangsheng, Chen Yanqiao","doi":"10.1109/ICMSP53480.2021.9513417","DOIUrl":null,"url":null,"abstract":"It is difficult to identify the monitoring parameters of the cooling pipeline system with seawater as medium when the pipeline is partially blocked and small leakage occurs. The control of condenser vacuum leads to the action of the control valve, which will make the system produce output response. Through theoretical analysis, the output response of the pipeline in different working conditions is different, which makes fault diagnosis possible. Therefore, this paper uses Flowmaster software to establish the models of normal working conditions, partial blocking working conditions and small leakage working conditions. The pressure variation in front of the condenser is simulated, and it is found that the pressure variation under the three working conditions is quite different, which indicates that the fault diagnosis can be carried out by identifying the pressure waveform. 400 groups of experimental data are obtained through simulation. Then the fault diagnosis system established by two different input methods of BP neural network is compared, and it is found that the iterations of taking the time series as the network input are less, the accuracy is as high as 96%.","PeriodicalId":153663,"journal":{"name":"2021 3rd International Conference on Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 3rd International Conference on Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMSP53480.2021.9513417","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract

It is difficult to identify the monitoring parameters of the cooling pipeline system with seawater as medium when the pipeline is partially blocked and small leakage occurs. The control of condenser vacuum leads to the action of the control valve, which will make the system produce output response. Through theoretical analysis, the output response of the pipeline in different working conditions is different, which makes fault diagnosis possible. Therefore, this paper uses Flowmaster software to establish the models of normal working conditions, partial blocking working conditions and small leakage working conditions. The pressure variation in front of the condenser is simulated, and it is found that the pressure variation under the three working conditions is quite different, which indicates that the fault diagnosis can be carried out by identifying the pressure waveform. 400 groups of experimental data are obtained through simulation. Then the fault diagnosis system established by two different input methods of BP neural network is compared, and it is found that the iterations of taking the time series as the network input are less, the accuracy is as high as 96%.
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冷却管路系统故障诊断
以海水为介质的冷却管道系统在管道部分堵塞、发生小泄漏时,监测参数难以识别。冷凝器真空度的控制引起控制阀的动作,使系统产生输出响应。通过理论分析,不同工况下管道的输出响应是不同的,为故障诊断提供了可能。因此,本文利用Flowmaster软件建立了正常工况、部分堵塞工况和小泄漏工况的模型。对冷凝器前压力变化进行仿真,发现三种工况下的压力变化差异较大,表明可以通过识别压力波形进行故障诊断。通过仿真得到了400组实验数据。然后比较了两种不同输入方式建立的BP神经网络故障诊断系统,发现以时间序列作为网络输入的迭代次数较少,准确率高达96%。
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