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论文:2020,Vol:38,Issue(6):1308-1315 |
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引用本文： |
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石家魁, 姚坤, 付俊丰, 居国腾, 李丰均, 万杰. 50 MW汽轮机缸体偏心摆动故障预警方法[J]. 西北工业大学学报 |
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SHI Jiakui, YAO Kun, FU Junfeng, JU Guoteng, LI Fengjun, WAN Jie. Study on Early Warning Method of Cylinder Block's Off-Center Position and Swing Failure for 50 MW Steam Turbine[J]. Northwestern polytechnical university |
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| 50 MW汽轮机缸体偏心摆动故障预警方法 |
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| 石家魁1, 姚坤2, 付俊丰1, 居国腾3, 李丰均3, 万杰1 |
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1. 哈尔滨工业大学 电气工程及自动化学院, 黑龙江 哈尔滨 150001;
2. 哈尔滨工业大学(深圳) 机电工程与自动化学院, 广东 深圳 518055;
3. 浙江浙能绍兴滨海热电有限责任公司, 浙江 绍兴 312073 |
| 摘要: |
| 50 MW背压机组汽轮机由于缸体结构特殊性，在启动及运行过程中存在偏心摆动故障风险，并且缺乏有效的监测预警手段，严重影响了机组运行的安全性。因此，缸体偏心摆动故障的提前预警非常重要。通过设计缸体偏移放大机构进行故障的监测，获取建立数理模型所需的偏心摆动数据。选择神经网络进行数据驱动建模，得到了2个时序预测模型，并研究了输入输出参数对预测精度的影响。通过选取合理的预警值及判定规则，实现了对偏心摆动故障的有效预警，开发了实时监测及故障预警装置。所提方法对避免50 MW机组偏心摆动故障带来的设备损坏具有重要的工程价值。 |
| 关键词:
50 MW背压机组
汽缸偏心摆动
故障预警
振动
监测装置
神经网络
数据驱动模型
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| Study on Early Warning Method of Cylinder Block's Off-Center Position and Swing Failure for 50 MW Steam Turbine |
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| SHI Jiakui1, YAO Kun2, FU Junfeng1, JU Guoteng3, LI Fengjun3, WAN Jie1 |
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1. School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China;
2. School of Mechanical Engineering and Automation, Harbin Institute of Technology(Shenzhen), Shenzhen 518055, China;
3. Zhejiang Zheneng Shaoxing Binhai Thermal Power Co., Ltd, Shaoxing 312073, China |
| Abstract: |
| Due to the special structure of the cylinder block, there is an off-center position and swing fault in the process of start-up and operation of the 50 MW extraction unit. Moreover, the lack of effective monitoring and early warning means seriously affects the safety of the unit operation. Therefore, it is very important to forewarn the fault of cylinder off-center position and swing. First of all, through the design of cylinder block offset amplifying mechanism for fault monitoring, the data of eccentric swing required for establishing mathematical model is obtained. Then, neural network is selected for data-driven modeling, two time series prediction models are obtained, and the influence of input and output parameters on the prediction accuracy is studied. Finally, by selecting reasonable early warning value and decision rules, an effective early warning of off-center position and swing fault is realized, and a monitoring device for real-time monitoring and fault early warning is developed. The actual application effect shows that this early warning method has important engineering value to avoid equipment damage caused by the swing fault for 50 MW unit. |
| Key words:
50 MW back pressure unit
cylinder eccentric swing
fault early warning
vibration
monitoring device
neural network
data-driven model
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| 收稿日期: 2020-04-23
修回日期:
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| DOI: 10.1051/jnwpu/20203861308 |
| 基金项目: 国家重点研发计划项目（2017YFB0902101）、浙江省能源集团有限公司科技项目（ZNKJ-2019-009）与赛尔网络下一代互联网技术创新项目（NGIICS20190801）资助 |
| 通讯作者: 姚坤(1990-),哈尔滨工业大学(深圳)博士研究生,主要从事数据挖掘与故障诊断研究。e-mail:19b353009@stu.hit.edu.cn
Email：19b353009@stu.hit.edu.cn |
| 作者简介: 石家魁(1988-),哈尔滨工业大学博士研究生,主要从事大型火电机组协调优化与控制技术研究。
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| 作者相关文章 |
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| 石家魁 在本刊中的所有文章 |
| 姚坤 在本刊中的所有文章 |
| 付俊丰 在本刊中的所有文章 |
| 居国腾 在本刊中的所有文章 |
| 李丰均 在本刊中的所有文章 |
| 万杰 在本刊中的所有文章 |
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