【作者】 戈志华；

【导师】 宋之平； 李如翔；

【作者基本信息】 华北电力大学 ， 热能工程， 2000， 博士

【摘要】 本论文基于非线性理论，围绕汽轮发电机组运行状态监测和状态评价，对典型振动故障进行了系统的理论和实验研究。 分析现场的实际碰摩故障；引入混沌理论，系统地研究碰摩的故障特征，为碰摩故障的识别探寻有效判据；针对难于识别的中心孔进油故障，采用排除法给出识别该故障的依据；对裂纹和油膜振荡等非线性故障特进行实验分析的基础上，建立了振动故障数据库，作为故障诊断的知识来源。 针对汽轮机轴系振动故障的热参数背景，诊断过程中，考虑多种因素对故障的非线性耦合作用，进行热参数与振动频谱相结合的故障诊断方法研究。利用多参数多征兆进行故障识别，突破了只利用频谱征兆的局限。通过改进模糊神经网络诊断模型，建立RBF径向基函数组合子网络诊断系统，增强对故障分类的能力；并提出贡献性因子概念描述不同征兆对故障的作用大小，避免了输入征兆的冗余和诊断缺征兆。 目前制约诊断系统自学习的最大障碍是自学习知识样本的缺乏，本文通过样本的测度分析进行相关故障样本的归类，结合子网络遍历技术，提出一种故障样本获取新方法，通过改进的诊断模型有效地区分新故障，建立故障样本自动获取的理论方法，作为诊断系统自学习的知识样本来源。 利用混沌理论的相空间重构方法，建立非线性预测模型，用于汽轮发电机组振动状态预测和趋势分析。基于分形理论对汽轮发电机组状态的可预测性进行了系统分析，提出将关联维数作为可预测性的评价指标。结合现场实例，进行机组振动状态预测和中、长期趋势分析研究，为实现预知性维修进行前期的理论探讨； 采用分布式数据采集，在机组测量元件改动极小的情况下使大量温度和压力等测点引入数据采集系统；完成了具有联网功能的状态监测和故障诊断系统设计，并成功用于实际机组，弥补了该厂管理层缺少现场动态信息的空白。可进行基于频谱和热参数的振动故障诊断和运行趋势预测。更多还原

【Abstract】 The thesis devoted to turbo-generator unit condition monitoring and accessing in theory and application. Based on non-linear theory, the thesis explored the representative vibration faults by both theoretical and experimental methods systematically. The paper studied the characteristics of rub-impact phenomenon between rotor and stator in the power plant. Introducing chaotic theory, the paper discussed its feature deeply and developed a valid criterion to identify rub-impact fault between rotor and stator. Especially for fault of rotor filled with oil, which is difficult to be diagnosed, the thesis applied the exclusive approach to abstract the reliable judgements from examples in the field. Moreover, the characteristics of representative non-linear vibration faults such as crack and oil-whirl were researched experimentally in detail. Thus established vibration database can be applied by diagnostic system. Because of vibration faults corresponding to the thermal factors, the present diagnosis study considered non-linear coupling effects caused by various factors. Combining thermal symptoms with vibration spectrum, a new approach is developed for fault diagnosis based on multi-symptom, which overcome the limitation of only applying spectrum characteristics. Also the thesis improved the diagnosis model of fuzzy-neuron networks, established the composite sub-nets based on radial basis function. Thus the ability of the system to classify the faults was enhanced obviously. Besides, the paper put forward the concept contribution factor to describe the different function of different symptoms in different areas. The current fault diagnosis system can also avoid the common defect of symptom redundancy or symptom scarcity. At present, the greatest obstacle to restrict the development of self-learning for diagnosis is lack of applied knowledge. The correlating fault samples can be classified by calculating he measurement coefficient?between two signals. With the traversal technique of composite sub-nets, the thesis put forward a new method to acquire the standard samples of novel faults. By the improved diagnosis model, the system can identify the novel faults exactly. The acquired knowledge samples of novel faults can be considered as the knowledge source to realize self-learning function in diagnostic process and added to diagnosis system. Using the phase space reconstruction method of chaotic theory, the thesis built the non-linear predicting model, which is used to forecast the vibration condition and analyze the condition trend. Without any hypothesis, the model can ensure the studied signal does not distort. It is shown that the current method is better than the traditional means. Moreover, by exploring the fractal feature of vibration series systematically, the paper introduced the correlation dimension as the standard to evaluate the predicted ability of time series. Some examples such as vibration, temperature and vacuum of condenser were taken for forecasting and got well agreement with real signals. The paper is the primary research for predictive maintenance theoretically. The system collected various input signals separately of two sets of turbo- generator units, with the minimum alteration of measuring components in the field. The thesis established the distributed condition monitoring and fault diagnosis system, which was applied in Tianjin First Power Plant. The system can improve the operation and manag更多还原