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600MW汽轮机组振动故障诊断方法的研究

发布时间:2018-06-23 20:41

  本文选题:600MW汽轮机组 + 振动故障机理分析 ; 参考:《华北电力大学》2015年硕士论文


【摘要】:为了满足生活及生产用电量急剧增加的需求,人们需要不断地对汽轮发电机组容量进行改造及研制;但是,随着机组容量的不断增大,对于材料及运行环境的要求也会相应地提高,特别是在国家实行了“上大压小”政策之后,我国的主力发电机组的容量也进入了600MW时代,而接踵而来的问题便是细微的机组振动故障却带来广泛的影响,因此对其进行600MW汽轮机组的振动故障诊断就变得更加重要。本文针对600MW汽轮机组的振动,阐述了目前600MW汽轮机振动诊断的过程,和最新的研究现状。对于汽轮机振动的故障进行机理分析并且根据现场的实际运行状况进行解读。对于振动故障的特征提取分析,应用目前较为常用的时域分析、频域分析、时频域分析分别进行分析处理,研究针对与600MW大型汽轮机振动的特征提取方法,故障诊断方面采取的是目前研究较为热门的支持向量机的分类诊断方法,并应用交叉验证和网格搜素、粒子群优化算法、遗传算法对支持向量机进行参数寻优。最终根据自己的所掌握汽轮机机组的振动知识,选取对企业最为有益的汽轮机振动故障诊断系统。
[Abstract]:In order to meet the demand of increasing power consumption in daily life and production, people need to transform and develop the capacity of turbine-generator set constantly, but with the increasing of capacity of turbine generator, The requirements for materials and operating environment will also be raised accordingly, especially after the implementation of the policy of "high pressure and small pressure", the capacity of the main generating units in our country has also entered the 600MW era. However, the following problems are caused by minor vibration faults, so it is more important to diagnose the vibration faults of 600MW steam turbines. Aiming at the vibration of 600MW steam turbine, this paper expounds the process of vibration diagnosis of 600MW steam turbine at present, and the latest research status. The mechanism of turbine vibration fault is analyzed and interpreted according to the actual operation condition. For the feature extraction analysis of vibration fault, this paper applies the time domain analysis, frequency domain analysis and time frequency domain analysis to analyze and deal with the vibration characteristics of 600MW large steam turbine. In the aspect of fault diagnosis, a popular classification and diagnosis method of support vector machine (SVM) is adopted, and the parameters of SVM are optimized by cross-validation, mesh search, particle swarm optimization and genetic algorithm. Finally, according to the knowledge of turbine unit vibration, the most beneficial fault diagnosis system of steam turbine is selected.
【学位授予单位】:华北电力大学
【学位级别】:硕士
【学位授予年份】:2015
【分类号】:TM621

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3 陈进;信号处理在机械设备故障诊断中的应用(连载)[J];振动与冲击;1999年04期



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