基于支持向量机的汽轮机轴系振动故障智能诊断研究
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摘要
随着汽轮机向大型化、复杂化、高参数的方向发展,为保证设备的安全可靠运行,人们对设备状态监测与故障诊断技术的重视程度越来越高,诊断技术也开始朝着智能化的方向发展。支持向量机是建立在统计学习理论基础上的新型学习机器,为解决小样本的故障分类问题提供了有效手段。将支持向量机应用到汽轮机故障诊断领域,能够有效地提高故障诊断的准确率,对避免事故发生带来的巨大损失,提高经济效益和社会效益都具有十分重要的意义。
论文结合汽轮机常见的轴系振动故障,采用支持向量机方法对故障进行分类和预测,为研究更好的汽轮机故障诊断方法提供了依据。论文围绕基于支持向量机的智能故障诊断问题,针对数据预处理、故障特征提取、故障分类、故障建模与预测及汽轮机诊断系统的构建等方面开展了研究,主要研究成果有:
1、通过分析常用特征提取和选择方法,引入了主分量分析和基于核函数的特征提取方法,对汽轮机轴系振动故障进行特征提取,并采用针对故障类型的模糊化K-L变换,压缩故障数据的维数,降低支持向量机分类算法的运算复杂度,并通过仿真实验,验证了该方法能够有效地提高故障分类的准确率;
2、详细讨论了支持向量机方法在汽轮机故障诊断领域的具体应用,构造了基于支持向量机的故障多分类模型,实现了多类故障的一次性区分;
3、研究了支持向量回归在故障建模和故障预测方面的具体应用,并通过仿真实验,分析和比较了支持向量机与其他智能方法的优劣;
4、通过实际的汽轮机轴系振动故障数据,将支持向量机应用于故障分类和趋势预测,验证了基于支持向量机的智能故障诊断方法的有效性,为支持向量机的实用化提供了参考;
5、开发了一套汽轮机轴系振动数据采集与故障诊断系统,将支持向量机方法与模糊诊断功能引入到故障诊断软件中,利用支持向量回归的建模、辨识和预测能力,对轴系振动信号进行趋势分析。该系统能够在线采集故障数据,并进行离线的故障分析,实现了汽轮机组轴系振动故障的分类和发现早期轻微故障的目的。
With the development of large-scale, complex turbine of high parameter, people pay more and more attention to technology of state monitor and fault diagnosis to ensure safely running of the equipment, and the technology of fault diagnosis begins developing to intelligent diagnosis. Support Vector Machine (SVM) is a new kind of intelligent learning machine built on Statistical Learning Theory (SLT), and offers an effective means to solve the problem of fault classification with small samples. The application of SVM in turbine fault diagnosis is able to effectively improve veracity of fault diagnosis, and is significative to avoid incalculable loss of accident and enhance economic and social benefit.
The paper adopts method of SVM to solve the problems of fault classification and prediction according to familiar vibration fault of shafting, and suggests basis for means of fault diagnosis of turbine. The paper mainly works on data pre-processing, fault feature extraction, fault classification, fault modeling and prediction, turbine fault diagnosis system with method of intelligent fault diagnosis based on SVM. The main research achievements are showed in follows:
1. Method of feature extraction based on Principal Component Analysis and kernel function are imported to achieve feature extraction for turbine shafting fault, which uses fuzzy K-L transform to comprese feature dimensions of fault data to reduce compution complexity of SVM’s classification arithmetic. Simulation experiment shows that this method is able to effectively improve veracity of fault classification.
2. The paper discuss application of SVM in turbine fault diagnosis and constructs SVM muti-classification model to accomplish fault diagnosis with once operation according to several kinds of faults.
3. Simulation experiment of the application of Support Vector Regression (SVR) in fault modeling and prediction shows that fault diagnosis based on SVM is more effective than other intelligent method.
4. According to actual vibration fault data of turbin shafting, SVM methonds are used to fault classification and fault trend prediction, which validates the validity of the intelligent fault diagnosis based on SVM and offers reference for practicality of SVM.
5. A kit of turbine shafting vibration fault diagnosis system is designed and the method of SVM and fuzzy diagnosis are imported to the diagnosis software, which uses SVR’s ability of modeling and prediction to analyse the trend of shafting vibration signal. The diagnosis system is able to collect fault data on-line and analyse the fault off-line, which achieves classification of turbine shafting vibration fault and detect early slight fault.
论文结合汽轮机常见的轴系振动故障,采用支持向量机方法对故障进行分类和预测,为研究更好的汽轮机故障诊断方法提供了依据。论文围绕基于支持向量机的智能故障诊断问题,针对数据预处理、故障特征提取、故障分类、故障建模与预测及汽轮机诊断系统的构建等方面开展了研究,主要研究成果有:
1、通过分析常用特征提取和选择方法,引入了主分量分析和基于核函数的特征提取方法,对汽轮机轴系振动故障进行特征提取,并采用针对故障类型的模糊化K-L变换,压缩故障数据的维数,降低支持向量机分类算法的运算复杂度,并通过仿真实验,验证了该方法能够有效地提高故障分类的准确率;
2、详细讨论了支持向量机方法在汽轮机故障诊断领域的具体应用,构造了基于支持向量机的故障多分类模型,实现了多类故障的一次性区分;
3、研究了支持向量回归在故障建模和故障预测方面的具体应用,并通过仿真实验,分析和比较了支持向量机与其他智能方法的优劣;
4、通过实际的汽轮机轴系振动故障数据,将支持向量机应用于故障分类和趋势预测,验证了基于支持向量机的智能故障诊断方法的有效性,为支持向量机的实用化提供了参考;
5、开发了一套汽轮机轴系振动数据采集与故障诊断系统,将支持向量机方法与模糊诊断功能引入到故障诊断软件中,利用支持向量回归的建模、辨识和预测能力,对轴系振动信号进行趋势分析。该系统能够在线采集故障数据,并进行离线的故障分析,实现了汽轮机组轴系振动故障的分类和发现早期轻微故障的目的。
With the development of large-scale, complex turbine of high parameter, people pay more and more attention to technology of state monitor and fault diagnosis to ensure safely running of the equipment, and the technology of fault diagnosis begins developing to intelligent diagnosis. Support Vector Machine (SVM) is a new kind of intelligent learning machine built on Statistical Learning Theory (SLT), and offers an effective means to solve the problem of fault classification with small samples. The application of SVM in turbine fault diagnosis is able to effectively improve veracity of fault diagnosis, and is significative to avoid incalculable loss of accident and enhance economic and social benefit.
The paper adopts method of SVM to solve the problems of fault classification and prediction according to familiar vibration fault of shafting, and suggests basis for means of fault diagnosis of turbine. The paper mainly works on data pre-processing, fault feature extraction, fault classification, fault modeling and prediction, turbine fault diagnosis system with method of intelligent fault diagnosis based on SVM. The main research achievements are showed in follows:
1. Method of feature extraction based on Principal Component Analysis and kernel function are imported to achieve feature extraction for turbine shafting fault, which uses fuzzy K-L transform to comprese feature dimensions of fault data to reduce compution complexity of SVM’s classification arithmetic. Simulation experiment shows that this method is able to effectively improve veracity of fault classification.
2. The paper discuss application of SVM in turbine fault diagnosis and constructs SVM muti-classification model to accomplish fault diagnosis with once operation according to several kinds of faults.
3. Simulation experiment of the application of Support Vector Regression (SVR) in fault modeling and prediction shows that fault diagnosis based on SVM is more effective than other intelligent method.
4. According to actual vibration fault data of turbin shafting, SVM methonds are used to fault classification and fault trend prediction, which validates the validity of the intelligent fault diagnosis based on SVM and offers reference for practicality of SVM.
5. A kit of turbine shafting vibration fault diagnosis system is designed and the method of SVM and fuzzy diagnosis are imported to the diagnosis software, which uses SVR’s ability of modeling and prediction to analyse the trend of shafting vibration signal. The diagnosis system is able to collect fault data on-line and analyse the fault off-line, which achieves classification of turbine shafting vibration fault and detect early slight fault.
引文
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