融合支持向量机的水电机组混合智能故障诊断研究
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摘要
随着水力资源的不断开发,水电在电力能源结构中所占比重逐渐增大,作为水电生产过程核心设备的水电机组的结构日趋复杂,集成化程度越来越高,不同部件之间动力学行为相互影响、相互作用,机组振动问题日益突出,对电网的安全稳定运行造成的影响也日益凸显。因此,常规的水电机组振动故障诊断方法已经不能很好的适应于当前的工程实际,迫切需要采用一些有效的智能故障诊断方法对机组振动故障进行诊断,以提高机组故障诊断的准确性、智能性及鲁棒性。本文针对水轮发电机组故障诊断和工程应用中的关键科学问题,运用支持向量机理论进行水电机组振动故障诊断,深入研究了支持向量机的理论及工程应用,将先进信号处理技术与智能方法和支持向量机进行融合,使支持向量机与其它智能方法取长补短、优势互补,提出了若干融合支持向量机的水电机组混合智能故障诊断方法。论文的主要研究内容和创新性成果如下：
(1)充分研究了支持向量机的模型参数对其性能的影响,提出采用特征空间中的类均值距离作为衡量所选核函数参数优劣的准则,并在此基础上确定出多类支持向量机核参数的小而有效的搜索区间；在新的核参数搜索区间和惩罚因子的搜索区间上,利用一种具有自适应搜索因子的差分进化算法进行支持向量机参数组合寻优。工程应用结果表明所提出的方法能够有效诊断出机组的典型故障,具有一定的可行性和有效性。
(2)提出采用集合经验模态分解及基于集合经验模态分解的Hilbert谱与Hilbe1rt边际谱对水电机组尾水管压力脉动信号进行分析；重点研究了基于集合经验模态分解的本征模态函数能量熵与奇异值分解特征提取方法,利用本征模态函数能量熵判断机组是否运行于故障状态；如果机组运行于故障状态,将本征模态函数奇异值特征输入前述经参数优化的支持向量机进行故障类型诊断；工程应用表明所提方法能够识别出设备的多种运行工况,所提方法已被成功应用在松江河发电厂故障诊断系统中。
(3)采用模糊支持向量机进行水电机组故障诊断,模糊支持向量机在训练阶段对故障样本区别对待,能够有效消除孤立点和野点子对诊断结果的影响；在模糊支持向量机中采用一种模糊sigmoid核函数,对这种核函数的形式及优势进行了阐述；针对模糊支持向量机实际应用中隶属度函数难以确定的问题,提出一种反K近邻方法与类均值距离结合的隶属度函数确定方法；深入分析了一对一多类支持向量机,指出采用一对一方法将二类支持向量机推广到多类时,在训练阶段并不是所有的类别对形成的支持向量机对最终的决策分类都有贡献,即存在着计算冗余；在此基础上,提出一种改进的一对一方法以删除其中不必要的支持向量机的训练。将所提方法应用于水电机组振动故障诊断取得满意的诊断结果。
(4)针对传统故障诊断分类器不能诊断出机组的不确定信息的不足,提出一种新的支持向量机与粗糙集结合的故障诊断方法。所提方法充分考虑了支持向量机和粗糙集各自的优缺点,将二者有机融合,优势互补,利用粗糙集来描述支持向量机的分类间隔,采用粗糙集上下近似的概念描述故障的不确定信息,充分利用了支持向量机强大的泛化能力和粗糙集对不确定数据的较强建模能力。将所提方法应用在某水电机组的故障诊断中能够诊断出机组的耦合故障,或亚健康状态。对二滩水电站#3号机组上导摆度偏大问题进行了综合分析,分析结论为二滩水电站管理运行人员提供了有益指导,同时进一步说明对水电机组耦合故障进行诊断的必要性。
With the rapid development of hydropower resources, the proportion of hydropower energy in electrical energy structure is gradually increasing, and hydroelectric generator units (HGU) which are the key equipment of the hydropower production process are becoming more and more large-scale, complex, high-speed and high-power. Meanwhile, the integrated degree of the HGU is becoming higher, and different parts of HGU influence each other, which makes the dynamic behavior of HGU become more complex. Consenquently, the vibaration problem of the HGU has become increasingly prominent, and the vibrant influence on the safety and stability of the grid has become increasingly large. Hence, the conventional vibration fault diagnosis methods for HGU are not adapted to current engineering practice. There is an urgent need to adopt some effective intelligent fault diagnosis methods to improve the accuracy, intelligence and robustness of fault diagnosis for HGU. To solve the Key scientific issues in engineering applications of fault diagnosis for HGU, support vector machine (SVM) is used to diagnose the vibrant faults. The theory and engineering application of SVM are deeply researched. To compensate for the shortage of SVM in engineering practice and bring the performance of SVM into full play, advanced signal processing techniques and some other intelligent methods and SVM are fused to form some hybrid intelligent fault diagnosis methods. The main contents and innovative achievements of the paper are as follows:
(1) Model parameters selection for SVM and the influence it affected on the SVM performance are researched. Several forms of inter-class distance in the feature space (ICDF) are introduced and discussed. And, the ICDF is selected as one measure heuristic to shorten the range of the kernel parameter. Then, self-adaptive differential evolution algorithm is used to search the optimal parameter combination in the continuous intervals of kernel parameter and penalty parameter. Engineering results show that the proposed method can effectively locate the typical failure of the HGU. Thus, the proposed method is feasible and effective in fault diagnosis for HGU.
(2) Ensemble empirical mode decomposition (EEMD) and Hilbert spectrum and Hilbert marginal spectrum are used to analyze the draft tube pressure fluctuation. Then, the research is emphasized on feature extraction for fault vibrant signals. Two types of features, the EEMD energy entropy and singular values of the matrix whose rows are intrinsic mode functions (IMFs), are extracted. EEMD energy entropy is used to specify whether the machine has faults or not. If the machine has faults, singular values are input to multi-class SVM optimized by inter-cluster distance in the feature space (ICDSVM) to specify the fault type. The proposed method is applied to rotating machinery fault diagnosis. The results show that the proposed method can identify a variety of operating conditions. And, the proposed method has been successfully applied in the fault diagnosis system of Songjianghe power plants.
(3) Fuzzy SVM is used to diagnose faults of HGU. In the training phase, by giving different weights for different training samples, fuzzy SVM can effectively eliminate the influence of outliers or noise points for fault diagnosis. Fuzzy sigmoid kernel function is used in the fuzzy SVM, and the advantage of this fuzzy sigmoid kernel function is also described. To solve the problem that the membership function (MF) of fuzzy SVM is difficult to determine in applications, a new method based on reverse k-nearest neighbor algorithm (RkNN) and euclidean distance between class means is proposed to determine the MF for fuzzy SVM. On the basis of deep analysis of one-against-one multi-class SVM, we point out that not all SVM training is necessary for the final decision surface. To remove unnecessary SVM training, one form of improved one-against-one multi-class SVM is proposed. The proposed method is used in fault diagnosis for HGU, and the results are satisfactory.
(4) Traditional fault diagnosis classifier can not reflect the uncertain information in fault pattern recognition. To overcome this disadvantage, a novel classifier based on rough set and multi-class SVM is proposed. The proposed method takes full account of the advantages and disadvantages of SVM and rough set theory, and it is an organic integration of these two methods. In the proposed method, the essential ideas of rough set: upper approximation and lower approximation are used to describe the classification results of SVM, which can well describe the uncertain information in fault diagnosis for HGU. Hence, the proposed method takes full advantage of the strong modeling capabilities for uncertain data of rough set and powerful generalization ability of SVM. Engeering application shows that the proposed method can well diagnose coupled faults and Sub-health state of HGU. We also conduct a comprehensive analysis on the "too large swing" problem in upper guide bearing of # 3 unit of the Ertan Hydropower Station. The analysis conclusions provide useful guidance for the manager and operator of the Ertan Hydropower Station, also further illustrate the need of fault diagnosis for coupling faults of HGU.
(1)充分研究了支持向量机的模型参数对其性能的影响,提出采用特征空间中的类均值距离作为衡量所选核函数参数优劣的准则,并在此基础上确定出多类支持向量机核参数的小而有效的搜索区间；在新的核参数搜索区间和惩罚因子的搜索区间上,利用一种具有自适应搜索因子的差分进化算法进行支持向量机参数组合寻优。工程应用结果表明所提出的方法能够有效诊断出机组的典型故障,具有一定的可行性和有效性。
(2)提出采用集合经验模态分解及基于集合经验模态分解的Hilbert谱与Hilbe1rt边际谱对水电机组尾水管压力脉动信号进行分析；重点研究了基于集合经验模态分解的本征模态函数能量熵与奇异值分解特征提取方法,利用本征模态函数能量熵判断机组是否运行于故障状态；如果机组运行于故障状态,将本征模态函数奇异值特征输入前述经参数优化的支持向量机进行故障类型诊断；工程应用表明所提方法能够识别出设备的多种运行工况,所提方法已被成功应用在松江河发电厂故障诊断系统中。
(3)采用模糊支持向量机进行水电机组故障诊断,模糊支持向量机在训练阶段对故障样本区别对待,能够有效消除孤立点和野点子对诊断结果的影响；在模糊支持向量机中采用一种模糊sigmoid核函数,对这种核函数的形式及优势进行了阐述；针对模糊支持向量机实际应用中隶属度函数难以确定的问题,提出一种反K近邻方法与类均值距离结合的隶属度函数确定方法；深入分析了一对一多类支持向量机,指出采用一对一方法将二类支持向量机推广到多类时,在训练阶段并不是所有的类别对形成的支持向量机对最终的决策分类都有贡献,即存在着计算冗余；在此基础上,提出一种改进的一对一方法以删除其中不必要的支持向量机的训练。将所提方法应用于水电机组振动故障诊断取得满意的诊断结果。
(4)针对传统故障诊断分类器不能诊断出机组的不确定信息的不足,提出一种新的支持向量机与粗糙集结合的故障诊断方法。所提方法充分考虑了支持向量机和粗糙集各自的优缺点,将二者有机融合,优势互补,利用粗糙集来描述支持向量机的分类间隔,采用粗糙集上下近似的概念描述故障的不确定信息,充分利用了支持向量机强大的泛化能力和粗糙集对不确定数据的较强建模能力。将所提方法应用在某水电机组的故障诊断中能够诊断出机组的耦合故障,或亚健康状态。对二滩水电站#3号机组上导摆度偏大问题进行了综合分析,分析结论为二滩水电站管理运行人员提供了有益指导,同时进一步说明对水电机组耦合故障进行诊断的必要性。
With the rapid development of hydropower resources, the proportion of hydropower energy in electrical energy structure is gradually increasing, and hydroelectric generator units (HGU) which are the key equipment of the hydropower production process are becoming more and more large-scale, complex, high-speed and high-power. Meanwhile, the integrated degree of the HGU is becoming higher, and different parts of HGU influence each other, which makes the dynamic behavior of HGU become more complex. Consenquently, the vibaration problem of the HGU has become increasingly prominent, and the vibrant influence on the safety and stability of the grid has become increasingly large. Hence, the conventional vibration fault diagnosis methods for HGU are not adapted to current engineering practice. There is an urgent need to adopt some effective intelligent fault diagnosis methods to improve the accuracy, intelligence and robustness of fault diagnosis for HGU. To solve the Key scientific issues in engineering applications of fault diagnosis for HGU, support vector machine (SVM) is used to diagnose the vibrant faults. The theory and engineering application of SVM are deeply researched. To compensate for the shortage of SVM in engineering practice and bring the performance of SVM into full play, advanced signal processing techniques and some other intelligent methods and SVM are fused to form some hybrid intelligent fault diagnosis methods. The main contents and innovative achievements of the paper are as follows:
(1) Model parameters selection for SVM and the influence it affected on the SVM performance are researched. Several forms of inter-class distance in the feature space (ICDF) are introduced and discussed. And, the ICDF is selected as one measure heuristic to shorten the range of the kernel parameter. Then, self-adaptive differential evolution algorithm is used to search the optimal parameter combination in the continuous intervals of kernel parameter and penalty parameter. Engineering results show that the proposed method can effectively locate the typical failure of the HGU. Thus, the proposed method is feasible and effective in fault diagnosis for HGU.
(2) Ensemble empirical mode decomposition (EEMD) and Hilbert spectrum and Hilbert marginal spectrum are used to analyze the draft tube pressure fluctuation. Then, the research is emphasized on feature extraction for fault vibrant signals. Two types of features, the EEMD energy entropy and singular values of the matrix whose rows are intrinsic mode functions (IMFs), are extracted. EEMD energy entropy is used to specify whether the machine has faults or not. If the machine has faults, singular values are input to multi-class SVM optimized by inter-cluster distance in the feature space (ICDSVM) to specify the fault type. The proposed method is applied to rotating machinery fault diagnosis. The results show that the proposed method can identify a variety of operating conditions. And, the proposed method has been successfully applied in the fault diagnosis system of Songjianghe power plants.
(3) Fuzzy SVM is used to diagnose faults of HGU. In the training phase, by giving different weights for different training samples, fuzzy SVM can effectively eliminate the influence of outliers or noise points for fault diagnosis. Fuzzy sigmoid kernel function is used in the fuzzy SVM, and the advantage of this fuzzy sigmoid kernel function is also described. To solve the problem that the membership function (MF) of fuzzy SVM is difficult to determine in applications, a new method based on reverse k-nearest neighbor algorithm (RkNN) and euclidean distance between class means is proposed to determine the MF for fuzzy SVM. On the basis of deep analysis of one-against-one multi-class SVM, we point out that not all SVM training is necessary for the final decision surface. To remove unnecessary SVM training, one form of improved one-against-one multi-class SVM is proposed. The proposed method is used in fault diagnosis for HGU, and the results are satisfactory.
(4) Traditional fault diagnosis classifier can not reflect the uncertain information in fault pattern recognition. To overcome this disadvantage, a novel classifier based on rough set and multi-class SVM is proposed. The proposed method takes full account of the advantages and disadvantages of SVM and rough set theory, and it is an organic integration of these two methods. In the proposed method, the essential ideas of rough set: upper approximation and lower approximation are used to describe the classification results of SVM, which can well describe the uncertain information in fault diagnosis for HGU. Hence, the proposed method takes full advantage of the strong modeling capabilities for uncertain data of rough set and powerful generalization ability of SVM. Engeering application shows that the proposed method can well diagnose coupled faults and Sub-health state of HGU. We also conduct a comprehensive analysis on the "too large swing" problem in upper guide bearing of # 3 unit of the Ertan Hydropower Station. The analysis conclusions provide useful guidance for the manager and operator of the Ertan Hydropower Station, also further illustrate the need of fault diagnosis for coupling faults of HGU.
引文
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