基于振动分析的核电冷却剂泵故障诊断研究
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摘要
目前，中国已经成为世界上第二能源消费国。随着经济的持续增长，能源问题已经成为制约经济发展的重要因素。电力是能源问题中最为重要的环节。核电作为一种新能源和清洁能源为解决我国电力紧缺问题提供了有利条件。
核电的产生来自于核电站，核电站因其重要性和敏感性备受关注，尤其是日本核电站泄露事故以来，核电站的安全性再次成为热点问题。安全问题已经成为核电发展的核心问题，如果核电生产过程的安全不能得到保证，那么就根本谈不上核电发展问题。只有安全地生产核电，才能更好地发挥其经济和社会效益。
在核电站设备系统中，冷却剂泵是核岛中的核心机械设备。它能够驱动冷却剂在回路中循环流动实现热交换，它在核电站中的地位十分重要。冷却剂泵能否正常运行直接影响到整个核电设备的可靠性以及核电站的安全性。本文以核电站一回路中冷却剂泵的振动故障作为研究对象，采用自主研发的硬件数据采集平台，利用该平台采集冷却剂泵运行时产生的振动信号，并将这些振动信号进行分析处理从而建立故障诊断模型。本文围绕着核电冷却剂泵故障诊断的相关问题，在数据采集、特征提取、故障分类和智能故障诊断等方面展开了深入研究，主要的研究工作和成果包括：
(1)结合冷却剂泵运行特点，从构建振动数据硬件采集平台到特征提取以及故障分类，论文建立了一套完整的冷却剂泵故障诊断系统(FDSRCP)。目前在国内专门针对冷却剂泵故障诊断的研究处于初级阶段，还没有形成具有完整体系架构的系统。论文在冷却剂泵故障诊断方面进行了积极地研究和探索，为冷却剂泵故障诊断的发展提供了技术支撑。
(2)本文采用PXI面向仪器的总线接口平台，自主研发出数据采集模块PXI2120。它是一款采用16位高分辨率A/D，结合FPGA和PXI总线技术研发的新型振动信号采集模块。它能够对冷却剂泵产生的振动信号进行采集，通过PXI接口将数据传送给主机。PXI2120由采样模块、时钟模块、触发模块、存储模块以及PXI接口模块构成。它打破了国外厂商家在该领域的技术垄断。
(3)对原始数据进行特征提取是故障诊断中的关键步骤。论文在分析对比相关特征提取算法基础上，首次将振动烈度、小波能量谱和小波能量最大分解层功率谱作为特征提取参数引入到冷却剂泵故障诊断系统中，提出了VSWEPS分析方法，VSWEPS是将三者相结合的时频特征提取方法。本文将振动烈度作为时域特征提取参数。振动烈度能够反映出振动能量的大小，通过振动的速度值可求得振动烈度。频域分析是特征提取最重要的工具，因为设备运行时产生的振动信号为非平稳信号，所以传统Fourier变换并不适用。小波变换能够用于分析非平稳信号，但是它不能准确分析出振动信号中包含的微弱信号，而这些微弱信号往往是故障的早期征兆。为此，本文在小波变换的基础上，采用了小波能量谱和小波能量最大分解层功率谱相结合的分析方法对振动信号进行特征提取。实验结果表明，利用VSWEPS方法对振动信号进行特征提取能够取得良好的效果，尤其适用于故障发生的早期阶段。
(4)冷却剂泵故障进行分类时面临的一个难题是样本数据受到设备运行特点的制约，获取的数据是小样本数据集。传统人工神经网络并不适用于小样本数据集，而建立在统计学习理论基础上的支持向量机(SVM)算法特别适合小样本数据的训练学习。支持向量机利用核函数建立了非线性空间向线性空间的映射关系，很好地解决了数据维数灾难问题。本文深入全面地研究了支持向量机算法，详细讨论了其基本思想和实现算法，将支持向量机引入到冷却剂泵故障诊断中，通过对特征参数的分类识别，构造了基于C-支持向量机的多故障分类模型，它能够将多种振动故障类型一次性区分。实验结果验证了这种故障分类方法的有效性和实用性。
China has been becoming the second big country of energy consuming. As thecontinual growth of economy, it is the energy problem that has been the importantreason of constraining the economy development. In such a problem, power also hasthe most important role. Among all kinds of power, the nuclear power, as a new,cleaning energy, provides benefits for our shortage of power.
Nuclear power comes from nuclear power plant. The importance and sensitivityof nuclear power plant has got more concerns. Especially after the leak accident ofnuclear power plant in Japan, the safety of nuclear power plant became hot issuesonce again. The safety has been a problem that must be resolved in the developmentof nuclear power. If the safety of nuclear power’s generation could not be guaranteed,not to mention the development that will go on. It is only the safe generation ofnuclear power that can make the new energy sustainable and the economy and societybenefit from it.
Among all the devices in nuclear power plant, the core machinery equipment isreactor coolant pump in nuclear island. It also plays an important role in nuclearpower plant, because it can make the heat exchange by driving the coolant flowcircularly in a loop. Whether the reactor coolant pump runs normally or not directlyaffects the reliability of all the nuclear power devices and the safety of nuclear powerplant. The research object of this thesis is the vibration fault of reactor coolant pumpin the primary loop in nuclear power plant. By using the hardware data acquisitionplatform we made by ourselves, some vibration signals are acquired when reactorcoolant pump is running. According to these vibration signals, a fault diagnosis modelis built. The thesis focuses on the fault diagnosis of reactor coolant pump, studyingfurther on data acquisition, feature extraction, fault classification and intelligent fault diagnosis. The main researches and conclusions include:
(1) A completed FDSRCP system is proposed in this thesis, based on thecharacters of reactor coolant pump, building the hardware data acquisition platform,feature extraction and fault classification. Currently, the study of the fault diagnosis ofreactor coolant pump in China is still in its initial stage, a complete architecturesystem is not designed. This thesis gave a positive research on the fault diagnosis forreactor coolant pump in nuclear power plant. It provided technical support for thedevelopment of fault diagnosis for reactor coolant pump.
(2) We developed a high precision data acquisition module PXI2120byourselves based on the general bus platform of PXI instrument-oriented. PXI2120issuch a new data acquisition module that uses16-bits high resolution A/D, combingthe technologies of FPGA with PXI. This platform can acquire the vibration signalswhen reactor coolant pump is running. These collected data will be sent to maincomputer through the PXI interface. All the designs of PXI2120are independentlydeveloped, including the design and implementation of sample module, timebasemodule, trigger module, stored module and PXI interface module. PXI2120breaks thetechnological monopoly of foreign companies in the domain.
(3) Feature extraction from original data is the key step of fault diagnosis. In thisthesis, vibration severity, wavelet energy spectrum and power spectrum of waveletenergy maximum decomposition level are firstly used as feature parameters in thefault diagnosis system for reactor coolant pump, based on the comparison and analysisof related algorithms of feature extraction. We proposed a method of featureextraction named VSWEPS which is a method of time-frequency. Vibration severity issuitable for feature extraction in time-domain. Vibration severity that can get from thevalue of speed is able to affect how much the vibration energy is. That is why thevibration severity is as a parameter of feature extraction in time-domain. Frequencyanalysis is one of the most important tools in feature extraction, because the vibrationsignals from running devices are non-stationary signal, so traditional Fouriertransform can not be used here. Though wavelet transform can be used to analyzenon-stationary signal, it has no ability to accurate analyze faint signal in vibrationsignals. The faint signal is so important that it is often as a sign of fault. For thisreason, the methods of time-wavelet energy spectrum and power spectrum of waveletenergy maximum decomposition level are employed as a method of feature extractionfor vibration signals based on wavelet transform in this thesis. The experimentsshowed that better effects can be obtained by using VSWEPS as feature extraction for vibration signals, especially at the early stage that a fault would be happened.
(4) The classification for fault of reactor coolant pump faces to a problem thatthe sample data is constrained by the characteristic of device. The collecting of datasetis just small sample dataset. The support vector machine (SVM) algorithm based onstatistical learning theory is suitable for training and learning of small sample dataset.SVM uses kernel function to build a mapping relation from nonlinear space to linearspace, which gives a better solution to the problem of dimension disaster. The SVMalgorithms are comprehensively further studied in this thesis. The basic ideas andimplemental algorithms of SVM are discussed in detail. The SVM has beenintroduced into the fault diagnosis of reactor coolant pump. A multi-faultsclassification model based on C-SVM is also generated by recognizing the categoriesof feature parameters. This model can distinguish the categories of many vibrationfaults in one time. The experiments proved the effectiveness and practicality of theSVM-based method of classification.
核电的产生来自于核电站，核电站因其重要性和敏感性备受关注，尤其是日本核电站泄露事故以来，核电站的安全性再次成为热点问题。安全问题已经成为核电发展的核心问题，如果核电生产过程的安全不能得到保证，那么就根本谈不上核电发展问题。只有安全地生产核电，才能更好地发挥其经济和社会效益。
在核电站设备系统中，冷却剂泵是核岛中的核心机械设备。它能够驱动冷却剂在回路中循环流动实现热交换，它在核电站中的地位十分重要。冷却剂泵能否正常运行直接影响到整个核电设备的可靠性以及核电站的安全性。本文以核电站一回路中冷却剂泵的振动故障作为研究对象，采用自主研发的硬件数据采集平台，利用该平台采集冷却剂泵运行时产生的振动信号，并将这些振动信号进行分析处理从而建立故障诊断模型。本文围绕着核电冷却剂泵故障诊断的相关问题，在数据采集、特征提取、故障分类和智能故障诊断等方面展开了深入研究，主要的研究工作和成果包括：
(1)结合冷却剂泵运行特点，从构建振动数据硬件采集平台到特征提取以及故障分类，论文建立了一套完整的冷却剂泵故障诊断系统(FDSRCP)。目前在国内专门针对冷却剂泵故障诊断的研究处于初级阶段，还没有形成具有完整体系架构的系统。论文在冷却剂泵故障诊断方面进行了积极地研究和探索，为冷却剂泵故障诊断的发展提供了技术支撑。
(2)本文采用PXI面向仪器的总线接口平台，自主研发出数据采集模块PXI2120。它是一款采用16位高分辨率A/D，结合FPGA和PXI总线技术研发的新型振动信号采集模块。它能够对冷却剂泵产生的振动信号进行采集，通过PXI接口将数据传送给主机。PXI2120由采样模块、时钟模块、触发模块、存储模块以及PXI接口模块构成。它打破了国外厂商家在该领域的技术垄断。
(3)对原始数据进行特征提取是故障诊断中的关键步骤。论文在分析对比相关特征提取算法基础上，首次将振动烈度、小波能量谱和小波能量最大分解层功率谱作为特征提取参数引入到冷却剂泵故障诊断系统中，提出了VSWEPS分析方法，VSWEPS是将三者相结合的时频特征提取方法。本文将振动烈度作为时域特征提取参数。振动烈度能够反映出振动能量的大小，通过振动的速度值可求得振动烈度。频域分析是特征提取最重要的工具，因为设备运行时产生的振动信号为非平稳信号，所以传统Fourier变换并不适用。小波变换能够用于分析非平稳信号，但是它不能准确分析出振动信号中包含的微弱信号，而这些微弱信号往往是故障的早期征兆。为此，本文在小波变换的基础上，采用了小波能量谱和小波能量最大分解层功率谱相结合的分析方法对振动信号进行特征提取。实验结果表明，利用VSWEPS方法对振动信号进行特征提取能够取得良好的效果，尤其适用于故障发生的早期阶段。
(4)冷却剂泵故障进行分类时面临的一个难题是样本数据受到设备运行特点的制约，获取的数据是小样本数据集。传统人工神经网络并不适用于小样本数据集，而建立在统计学习理论基础上的支持向量机(SVM)算法特别适合小样本数据的训练学习。支持向量机利用核函数建立了非线性空间向线性空间的映射关系，很好地解决了数据维数灾难问题。本文深入全面地研究了支持向量机算法，详细讨论了其基本思想和实现算法，将支持向量机引入到冷却剂泵故障诊断中，通过对特征参数的分类识别，构造了基于C-支持向量机的多故障分类模型，它能够将多种振动故障类型一次性区分。实验结果验证了这种故障分类方法的有效性和实用性。
China has been becoming the second big country of energy consuming. As thecontinual growth of economy, it is the energy problem that has been the importantreason of constraining the economy development. In such a problem, power also hasthe most important role. Among all kinds of power, the nuclear power, as a new,cleaning energy, provides benefits for our shortage of power.
Nuclear power comes from nuclear power plant. The importance and sensitivityof nuclear power plant has got more concerns. Especially after the leak accident ofnuclear power plant in Japan, the safety of nuclear power plant became hot issuesonce again. The safety has been a problem that must be resolved in the developmentof nuclear power. If the safety of nuclear power’s generation could not be guaranteed,not to mention the development that will go on. It is only the safe generation ofnuclear power that can make the new energy sustainable and the economy and societybenefit from it.
Among all the devices in nuclear power plant, the core machinery equipment isreactor coolant pump in nuclear island. It also plays an important role in nuclearpower plant, because it can make the heat exchange by driving the coolant flowcircularly in a loop. Whether the reactor coolant pump runs normally or not directlyaffects the reliability of all the nuclear power devices and the safety of nuclear powerplant. The research object of this thesis is the vibration fault of reactor coolant pumpin the primary loop in nuclear power plant. By using the hardware data acquisitionplatform we made by ourselves, some vibration signals are acquired when reactorcoolant pump is running. According to these vibration signals, a fault diagnosis modelis built. The thesis focuses on the fault diagnosis of reactor coolant pump, studyingfurther on data acquisition, feature extraction, fault classification and intelligent fault diagnosis. The main researches and conclusions include:
(1) A completed FDSRCP system is proposed in this thesis, based on thecharacters of reactor coolant pump, building the hardware data acquisition platform,feature extraction and fault classification. Currently, the study of the fault diagnosis ofreactor coolant pump in China is still in its initial stage, a complete architecturesystem is not designed. This thesis gave a positive research on the fault diagnosis forreactor coolant pump in nuclear power plant. It provided technical support for thedevelopment of fault diagnosis for reactor coolant pump.
(2) We developed a high precision data acquisition module PXI2120byourselves based on the general bus platform of PXI instrument-oriented. PXI2120issuch a new data acquisition module that uses16-bits high resolution A/D, combingthe technologies of FPGA with PXI. This platform can acquire the vibration signalswhen reactor coolant pump is running. These collected data will be sent to maincomputer through the PXI interface. All the designs of PXI2120are independentlydeveloped, including the design and implementation of sample module, timebasemodule, trigger module, stored module and PXI interface module. PXI2120breaks thetechnological monopoly of foreign companies in the domain.
(3) Feature extraction from original data is the key step of fault diagnosis. In thisthesis, vibration severity, wavelet energy spectrum and power spectrum of waveletenergy maximum decomposition level are firstly used as feature parameters in thefault diagnosis system for reactor coolant pump, based on the comparison and analysisof related algorithms of feature extraction. We proposed a method of featureextraction named VSWEPS which is a method of time-frequency. Vibration severity issuitable for feature extraction in time-domain. Vibration severity that can get from thevalue of speed is able to affect how much the vibration energy is. That is why thevibration severity is as a parameter of feature extraction in time-domain. Frequencyanalysis is one of the most important tools in feature extraction, because the vibrationsignals from running devices are non-stationary signal, so traditional Fouriertransform can not be used here. Though wavelet transform can be used to analyzenon-stationary signal, it has no ability to accurate analyze faint signal in vibrationsignals. The faint signal is so important that it is often as a sign of fault. For thisreason, the methods of time-wavelet energy spectrum and power spectrum of waveletenergy maximum decomposition level are employed as a method of feature extractionfor vibration signals based on wavelet transform in this thesis. The experimentsshowed that better effects can be obtained by using VSWEPS as feature extraction for vibration signals, especially at the early stage that a fault would be happened.
(4) The classification for fault of reactor coolant pump faces to a problem thatthe sample data is constrained by the characteristic of device. The collecting of datasetis just small sample dataset. The support vector machine (SVM) algorithm based onstatistical learning theory is suitable for training and learning of small sample dataset.SVM uses kernel function to build a mapping relation from nonlinear space to linearspace, which gives a better solution to the problem of dimension disaster. The SVMalgorithms are comprehensively further studied in this thesis. The basic ideas andimplemental algorithms of SVM are discussed in detail. The SVM has beenintroduced into the fault diagnosis of reactor coolant pump. A multi-faultsclassification model based on C-SVM is also generated by recognizing the categoriesof feature parameters. This model can distinguish the categories of many vibrationfaults in one time. The experiments proved the effectiveness and practicality of theSVM-based method of classification.
引文
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