【作者】 张文斌；

【导师】 周晓军；

【作者基本信息】 浙江大学 ， 机械制造及其自动化， 2009， 博士

【摘要】 本文以汽轮发电机组为对象,对机组转子-轴承系统的状态趋势预测和故障诊断方法进行研究。首先,分析了汽轮发电机组常见典型故障的机理及其特征;其次,针对振动信号现场采集过程中易受噪声干扰的特点,提出了一种基于自适应结构元素的广义形态滤波方法对信号进行预处理;然后,针对反映汽轮发电机组工作状态的特征参数时间序列所具有的规律性特点,建立等维动态组合模型对机组的振动位移峰-峰值和基于不分层分析的谐波窗分解提取得到的特征量进行趋势预测;最后,针对轴心轨迹形状可以直观反映转子故障信息的特点,提出了一种基于不分层分析的谐波窗分解提纯轴心轨迹的故障诊断方法。第一章介绍了本文的选题背景及意义,综合论述了国内外汽轮发电机组状态趋势预测和故障诊断技术的研究现状,指出目前存在的一些问题,说明了本文的主要研究内容。第二章对汽轮发电机组常见典型故障进行了定性分析,研究了故障机理和特征,并简要介绍了目前火电厂旋转设备采用的振动标准,旨在为后续章节提供基础。第三章提出了一种基于自适应结构元素的广义形态滤波预处理方法。采用广义形态滤波对信号进行降噪预处理,无需预知信号的频谱特征,通过一小一大自适应的结构元素对信号进行形态学运算,即可抑制信号中的噪声干扰。文中详细论述了该方法的原理和构造过程,并通过仿真和实例检验了该方法对信号的预处理效果。第四章在对汽轮发电机组状态趋势预测任务和可预测性分析的基础上,构建了等维动态组合模型,对汽轮发电机组的振动位移峰-峰值建立了预测模型,并结合仿真和实例检验了该模型的预测精度。第五章在总结汽轮发电机组状态特征量提取方法的基础上,提出了一种基于不分层分析的谐波窗分解提取振动信号特征量的方法。采用不分层分析的谐波窗分解提取出反映机组运行工况的特征量,并对特征量建立等维动态组合预测模型,便于掌握机组状态变化的规律和发现设备早期故障的苗头。第六章针对轴心轨迹形状可以直观反映转子故障信息的特点,提出了一种基于不分层分析的谐波窗分解提纯轴心轨迹的故障诊断方法。通过对实测信号频率分量的判断,采用不分层分析的谐波窗分解提取信号中主要的频率成分,重构转子提纯的轴心轨迹,依据各典型故障的轴心轨迹特征进行故障诊断。第七章概括了全文的主要工作和本文的创新点,并对进一步的工作进行了展望。更多还原

【Abstract】 In this paper, methods of state trend prediction and fault diagnosis have been researched for turbo-generator unit. First, the common typical fault principle and feature are analyzed. Second, aiming at the practical signal is easy to be interrupted in acquisition field, a pre-processing method is proposed based on the adaptive structure element of generalized morphology filtering. Third, due to the state characteristic parameters have regularity, the equal dimension dynamic combination model is built to predict the vibration peak-to-peak values and characteristic values which are abstracted by harmonic window decomposition method which is no need for layer decomposition. Finally, due to the shape of rotor center’s orbit could express rotor’s fault directly, a fault diagnosis method by purifying rotor center’s orbit is proposed based on the harmonic window decomposition method which is no need for layer decomposition.Chapter 1 gives a comprehensive description about the research work’s background and significance, the current research status of state trend prediction and fault diagnosis skill of turbo-generator unit at home and abroad. Some problems existed in this area are pointed out and the main research content is given.Chapter 2 qualitatively analyzes the common typical faults of turbo-generator unit, studies the fault principle and feature. Then the vibration criterion for rotating machinery used in power plant is introduced, it supplies basis for latter chapters.Chapter 3 puts forward a pre-processing method based on the adaptive structure element of generalized morphology filtering. With this method, there is no need to know the spectrum feature of original signal when de-noising. By using a small structure and a big one in morphology processing, the noise interferences will be eliminated. The detailed principle and construction are given, and then the effectiveness of this method has been proved by simulation and practical data processing.Chapter 4 builds an equal dimension dynamic combination model on the base of state trend prediction task and predictable analysis. Then the predictive model is built for vibration peak-to-peak values of turbo-generator unit. The predictive precision has been verified by simulation and practical results.Chapter 5 proposes a method to extract vibration characteristic values on the base of the harmonic window decomposition method which is no need for layer decomposition. Using this method to extract vibration characteristic values which express operation status, then the equal dimension dynamic combination model is built for these characteristic values. It is useful to master the state changing regularity and find the symptom of forepart fault.Chapter 6 puts forward a fault diagnosis method by purifying rotor center’s orbit based on the harmonic window decomposition method which is no need for layer decomposition, whose shape could express rotor’s fault directly. By recognizing frequency components of practical signal, the main frequency components are abstracted by harmonic window decomposition method, and then the purified rotor center’s orbit is reconstructed. In the end, the fault diagnosis will be done according to rotor center’s orbit of the typical fault.The last chapter summaries the main research conclusion and key innovative points, and then further research work is put forward.更多还原