基于支持向量机的水电机组故障诊断研究
|
摘要
随着现代工业化进程的迅猛发展,工业设备正朝着精密化、复杂化以及高度集成自动化的方向不断发展,设备的安全、稳定运行对企业生产具有极其重要的意义。水电机组作为水电企业生产过程中的核心关键设备,其健康状态直接关系到水电企业生产的安全性和经济效益;另一方面,水电企业的检修方式正朝着状态检修的新机制转变。因而,通过监测水电机组运行状态,实现对水电机组健康状态的准确分析和水电机组故障模式的有效判断,对于水电企业生产与状态检修体制都具有积极的意义。在综合分析水电机组故障诊断研究方法的发展状况,针对水电机组振动故障研究中存在的小样本问题,以及水电机组振动信号的非线性与非平稳特性,本论文结合正处于发展当中的支持向量机诊断方法,对支持向量机方法在水电机组故障模式分类、状态趋势预测、诊断模型参数选择等多个方面的问题进行了系统的研究。
论文首先介绍了支持向量机方法的基本理论,分析了支持向量机标准算法在处理分类与回归问题的基本原理与算法。阐述了支持向量机标准算法的扩展形式——最小二乘支持向量机在分类与回归方面的基本原理与算法,并对比分析了这两种方法的差异,为支持向量机方法应用于水电机组故障诊断研究奠定了基础。
利用支持向量机方法进行了水电机组振动故障模式分类研究。故障诊断在本质上可以看作模式分类问题。因而结合支持向量机在模式分类问题中的优异性能,将支持向量机应用于水电机组故障诊断研究,提出基于小波分解与最小二乘支持向量机的水电机组振动故障分类模型。利用小波分解提取水电机组振动信号的能量特征值作为水电机组故障的特征向量,通过最小二乘支持向量机建立水电机组振动故障多类识别模型。实例分析验证了分类模型的有效性。
分析了设备运行状态发展的渐近性特征,具体阐述了水电机组状态预测的可行性,归纳总结了常用的预测方法,指出水电机组运行状态发展过程是一种从量变到质变的渐近性的发展过程。水电机组运行状态发展的渐近性特性是利用预测模型进行水电机组状态预测研究的重要理论依据。
在水电机组状态预测的可行性分析基础上,针对表征水电机组状态的振动信号序列,分析了水电机组振动状态序列相空间重构,以及与相空间重构相关的振动序列嵌入维数和延时参数等问题,归纳总结了预测模型的评价原则与误差评价函数;提出了基于小波分解与最小二乘支持向量机方法的集成预测模型。该集成预测模型利用小波分解方法将具有复杂趋势信息的振动信号序列分解成若干个具有较明显规律的子序列;然后利用最小二乘支持向量机实现子序列的预测;最后集成这些子序列的输出作为振动序列的预测结果。对比分析不同预测模型对振动序列的预测结果,集成预测模型具有较高的预测精度以及与原始信号良好的拟合程度,能较准确的反映出信号的峰值变化,对水电机组状态预警具有一定的参考意义。
模型参数选择是支持向量机研究中的重要内容,合适的模型参数才能使支持向量机模型获得满意的性能。结合遗传算法在复杂系统优化问题中具有的全局搜索能力,提出了基于遗传算法的最小二乘支持向量机预测模型的参数选择方法,并应用于水电机组振动状态预测研究。为水电机组故障诊断的支持向量机方法研究的模型参数选择提供了一种可行、有效的方法。
支持向量机具有坚实而完善的理论体系,但是其在理论方法的研究,以及在故障诊断领域中的应用仍然处于不断的发展过程中。因而,支持向量机方法在水电机组故障诊断理论与应用方面的研究均有待于进一步的深入和完善。
With the fast development of modern industrialization, industrial equipments are improving with high precision, complication and highly integrated automation and the safety and stability of equipments is very important to enterprise production. Hydroturbine generating units (HGU) are the key equipments in the hydropower station, which health condition will influence the safety and economical benefits of the station. At present, the maintenance system of hydropower station is transferring to the new system—condtion based maintenance (CBM). It is useful for the station production and CBM system that the correct condition analysis and efficient fault mode classification of the HGU can be done with the condition monitoring in the station. According to the small sample problems in the HGU fault diagnois and the nonlinear and nonstability characteristics in the HGU vibration, the support vector machines (SVM) method is introduced in HGU fault diagnosis research in this thesis. The research with SVM on fault pattern classification, condition trend prediction and parameter selection in HGU fault diagnosis is done systematically.
Firstly, the basic theory of SVM is introduced and the algorithms of classical SVM in classification and regression are analysed. Least squares support vector machines (LS-SVM) method, an extension version of classical SVM, is presented with the algorithms in classification and regression. Then the differences between SVM and LS-SVM are analysed. All of the algorithms and comparisons are the foundations for the application of LS-SVM in HGU fault diagnosis.
Fault diagnosis can be regarded as pattern classification essentially. With the excellent performance of SVM in pattern classification, a vibration fault pattern classification model is proposed based on wavelet decomposition and LS-SVM. With the energy feature vectors extracted from the HGU vibration signals, the multi-fault pattern classification model is built on the LS-SVM, which validity is testified by samples.
The asymptotic characteristics of equipment running condion are analysed. The feasibility of prediction of HGU condition is proposed in detail. The general prediction methods are summarized. It is indicated that the condition trend evolution of HGU is a process from quantitative change to qualitative change. The asymptotic characteristics of HGU provide convincing refernce for the prediction of HGU condition trend with prediction methods.
According to the vibration signal series as the representation of HGU condtion, the phase space reconstruction, embedding dimensions and time lags problems are discussed. Estimation rules and functions of prediction models are summarized. Based on the feasibility analysis of HGU condition prediction, a hybrid prediction model based on wavelet decomposition and LS-SVM is proposed. In this hybrid model, vibration signal series are divided into serval subseries with obvious tendency characteristics through wavelet decomposition. Then the tendency of these subseries is forecated with LS-SVM respectively. Finally, these prediction outputs are summed up as the prediction results of original vibration signal series. Compared with other different prediction models, the hybrid model gains higher prediction accuracy and predicts the peak value change in the signal, which is useful to the HGU condition precaution.
Parameter selection is an important part in SVM research; proper parameters of the SVM model only gain the perfect performance. With the full search ability of genetic algorithm (GA) in complex problems optimization, a parameter selection method of a LS-SVM prediction model based on GA is given, which is applied in HGU condition prediction research and provides a feasible and valid method for parameter selecetion of support vector machines based model in HGU fault diagnosis.
Support vector machines are based on fully developed theory. But the theory studies and applications in fault diagnosis of support vector machines are still in developing. So the research of support vector machines in HGU fault diagnosis need to be further studied.
论文首先介绍了支持向量机方法的基本理论,分析了支持向量机标准算法在处理分类与回归问题的基本原理与算法。阐述了支持向量机标准算法的扩展形式——最小二乘支持向量机在分类与回归方面的基本原理与算法,并对比分析了这两种方法的差异,为支持向量机方法应用于水电机组故障诊断研究奠定了基础。
利用支持向量机方法进行了水电机组振动故障模式分类研究。故障诊断在本质上可以看作模式分类问题。因而结合支持向量机在模式分类问题中的优异性能,将支持向量机应用于水电机组故障诊断研究,提出基于小波分解与最小二乘支持向量机的水电机组振动故障分类模型。利用小波分解提取水电机组振动信号的能量特征值作为水电机组故障的特征向量,通过最小二乘支持向量机建立水电机组振动故障多类识别模型。实例分析验证了分类模型的有效性。
分析了设备运行状态发展的渐近性特征,具体阐述了水电机组状态预测的可行性,归纳总结了常用的预测方法,指出水电机组运行状态发展过程是一种从量变到质变的渐近性的发展过程。水电机组运行状态发展的渐近性特性是利用预测模型进行水电机组状态预测研究的重要理论依据。
在水电机组状态预测的可行性分析基础上,针对表征水电机组状态的振动信号序列,分析了水电机组振动状态序列相空间重构,以及与相空间重构相关的振动序列嵌入维数和延时参数等问题,归纳总结了预测模型的评价原则与误差评价函数;提出了基于小波分解与最小二乘支持向量机方法的集成预测模型。该集成预测模型利用小波分解方法将具有复杂趋势信息的振动信号序列分解成若干个具有较明显规律的子序列;然后利用最小二乘支持向量机实现子序列的预测;最后集成这些子序列的输出作为振动序列的预测结果。对比分析不同预测模型对振动序列的预测结果,集成预测模型具有较高的预测精度以及与原始信号良好的拟合程度,能较准确的反映出信号的峰值变化,对水电机组状态预警具有一定的参考意义。
模型参数选择是支持向量机研究中的重要内容,合适的模型参数才能使支持向量机模型获得满意的性能。结合遗传算法在复杂系统优化问题中具有的全局搜索能力,提出了基于遗传算法的最小二乘支持向量机预测模型的参数选择方法,并应用于水电机组振动状态预测研究。为水电机组故障诊断的支持向量机方法研究的模型参数选择提供了一种可行、有效的方法。
支持向量机具有坚实而完善的理论体系,但是其在理论方法的研究,以及在故障诊断领域中的应用仍然处于不断的发展过程中。因而,支持向量机方法在水电机组故障诊断理论与应用方面的研究均有待于进一步的深入和完善。
With the fast development of modern industrialization, industrial equipments are improving with high precision, complication and highly integrated automation and the safety and stability of equipments is very important to enterprise production. Hydroturbine generating units (HGU) are the key equipments in the hydropower station, which health condition will influence the safety and economical benefits of the station. At present, the maintenance system of hydropower station is transferring to the new system—condtion based maintenance (CBM). It is useful for the station production and CBM system that the correct condition analysis and efficient fault mode classification of the HGU can be done with the condition monitoring in the station. According to the small sample problems in the HGU fault diagnois and the nonlinear and nonstability characteristics in the HGU vibration, the support vector machines (SVM) method is introduced in HGU fault diagnosis research in this thesis. The research with SVM on fault pattern classification, condition trend prediction and parameter selection in HGU fault diagnosis is done systematically.
Firstly, the basic theory of SVM is introduced and the algorithms of classical SVM in classification and regression are analysed. Least squares support vector machines (LS-SVM) method, an extension version of classical SVM, is presented with the algorithms in classification and regression. Then the differences between SVM and LS-SVM are analysed. All of the algorithms and comparisons are the foundations for the application of LS-SVM in HGU fault diagnosis.
Fault diagnosis can be regarded as pattern classification essentially. With the excellent performance of SVM in pattern classification, a vibration fault pattern classification model is proposed based on wavelet decomposition and LS-SVM. With the energy feature vectors extracted from the HGU vibration signals, the multi-fault pattern classification model is built on the LS-SVM, which validity is testified by samples.
The asymptotic characteristics of equipment running condion are analysed. The feasibility of prediction of HGU condition is proposed in detail. The general prediction methods are summarized. It is indicated that the condition trend evolution of HGU is a process from quantitative change to qualitative change. The asymptotic characteristics of HGU provide convincing refernce for the prediction of HGU condition trend with prediction methods.
According to the vibration signal series as the representation of HGU condtion, the phase space reconstruction, embedding dimensions and time lags problems are discussed. Estimation rules and functions of prediction models are summarized. Based on the feasibility analysis of HGU condition prediction, a hybrid prediction model based on wavelet decomposition and LS-SVM is proposed. In this hybrid model, vibration signal series are divided into serval subseries with obvious tendency characteristics through wavelet decomposition. Then the tendency of these subseries is forecated with LS-SVM respectively. Finally, these prediction outputs are summed up as the prediction results of original vibration signal series. Compared with other different prediction models, the hybrid model gains higher prediction accuracy and predicts the peak value change in the signal, which is useful to the HGU condition precaution.
Parameter selection is an important part in SVM research; proper parameters of the SVM model only gain the perfect performance. With the full search ability of genetic algorithm (GA) in complex problems optimization, a parameter selection method of a LS-SVM prediction model based on GA is given, which is applied in HGU condition prediction research and provides a feasible and valid method for parameter selecetion of support vector machines based model in HGU fault diagnosis.
Support vector machines are based on fully developed theory. But the theory studies and applications in fault diagnosis of support vector machines are still in developing. So the research of support vector machines in HGU fault diagnosis need to be further studied.
引文
[1] Rehorn, A. G., Jiang, J., Orban, P. E. State-of-the-art methods and results in tool condition monitoring: a review. International Journal Of Advanced Manufacturing Technology. 2005, 26(7-8): 693-710
[2]梁为,李洛虎.机械故障诊断. (第1版).北京:煤炭工业出版社, 2005.
[3]赵林度.大型机电系统故障诊断技术. (第1版).北京:中国石化出版社, 2001.
[4]叶鲁卿.水力发电过程控制—理论、应用及其发展. (第1版).武汉:华中科技大学出版社, 2002.
[5] Nandi, S., Toliyat, H. A., Li, X. Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review. Energy Conversion, IEEE Transactions on. 2005, 20(4): 719-729
[6] Awadallah, M. A., Morcos, M. M. Application of AI tools in fault diagnosis of electrical machines and drives-an overview. Energy Conversion, IEEE Transaction on. 2003, 18(2): 245-251
[7] Dash, S., Venkatasubramanian, V. Challenges in the industrial applications of fault diagnostic systems. Computers & Chemical Engineering. 2000, 24(2-7): 785-791
[8]杨建东,莫剑,唐岳灏等.水电站过渡过程程序可视化设计的思路及实现.水电能源科学. 2005, 23(1): 57-59
[9]刘晓亭,刘昱.水电机组状态检修的实施及关键技术.水力发电. 2004, 30(4): 38-41
[10]何永勇,任继顺,陈伟, et al.水电机组远程状态监测、跟踪分析与故障诊断系统.清华大学学报(自然科学版). 2006, 46(5): 629-632
[11]傅闯.基于Multi-Agent的ICMMS建模及其预知维护子系统的研究: [博士学位论文].武汉:华中科技大学, 2003.
[12] Yang, S. K. A condition-based failure-prediction and processing-scheme for preventive maintenance. Reliability, IEEE Transactions on. 2003, 52(3): 373-383
[13] Durocher, D. B., Feldmeier, G. R. Predictive versus preventive maintenance. IndustryApplications Magazine, IEEE. 2004, 10(5): 12-21
[14] Fu, C., Ye, L., Liu, Y., et al. Predictive maintenance in intelligent-control-maintenance-management system for hydroelectric generating unit. Energy Conversion, IEEE Transaction on. 2004, 19(1): 179-186
[15]边肇祺,张学工.模式识别. (第2版).北京:清华大学出版社, 2000.
[16]黄文虎,夏松波.设备故障诊断原理、技术及应用. (第1版).北京:科学出版社, 1996.
[17]张来斌.机械设备故障诊断技术及方法. (第1版).北京:石油工业出版社, 2000.
[18]钟秉林,黄仁.机械故障诊断学. (第1版).北京:机械工业出版社, 1997.
[19]王道平.故障智能诊断系统的理论与方法. (第1版).北京:冶金工业出版社, 2001.
[20] Shi, W.-W., Yan, H.-S., Ma, K.-P. A new method of early fault diagnosis based on machine learning. in: Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on. Guangzhou, China: vol. 6, 2005. 3271-3276
[21] Wu, L.-H., Jiang, Y.-F., Huang, W., et al. The Intelligent Fault Diagnosis for Composite Systems Based on Machine Learning. in: Machine Learning and Cybernetics, 2006 International Conference on. Dalian, China: 2006. 571-575
[22] Murphey, Y. L., A., M. M., Chen, Z., et al. Model-based fault diagnosis in electric drives using machine learning. Mechatronics, IEEE/ASME Transactions on. 2006, 11(3): 290-303
[23] Ishigaki, T., Higuchi, T., Watanabe, K. Spectrum Classification for Early Fault Diagnosis of the LP Gas Pressure Regulator Based on the Kullback-Leibler Kernel. in: Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on. Maynooth, Ireland: 2006. 453-458
[24]吴今培,肖健华.智能故障诊断与专家系统. (第1版).北京:科学出版社, 1997.
[25]冯志鹏.计算智能在机械设备故障诊断中的应用研究: [博士学位论文].大连:大连理工大学, 2003.
[26]虞和济,侯广琳.故障诊断的专家系统. (第1版).北京:治金工业出版社, 1991.
[27] Purkait, P., Chakravorti, S. Time and frequency domain analyses based expert system for impulse fault diagnosis in transformers. Dielectrics and Electrical Insulation,IEEE Transactions on [see also Electrical Insulation, IEEE Transactions on]. 2002, 9(3): 433-445
[28] Yang, B.-S., Lim, D.-S., Tan, A. C. C. VIBEX: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table. Expert Systems with Applications. 2005, 28(4): 735-742
[29] Afgan, N. H., Carvalho, M. G., Pilavachi, P. A., et al. An expert system concept for diagnosis and monitoring of gas turbine combustion chambers. Applied Thermal Engineering. 2006, 26(7): 766-771
[30] Song, G., He, Y., Chu, F., et al. HYDES: A Web-based hydro turbine fault diagnosis system. Expert Systems with Applications. 2006, doi:10.1016/j.eswa.2006.10.017
[31] Venkat, V., King, C. A neural network methodology for process fault diagnosis. Journal of AIChE. 1989, 35(12): 1993-2002
[32] Mohamed, E. A., Abdelaziz, A. Y., Mostafa, A. S. A neural network-based scheme for fault diagnosis of power transformers. Electric Power Systems Research. 2005, 75(1): 29-39
[33] Li, B., Chow, M. Y., Tipsuwan, Y., et al. Neural-network-based motor rolling bearing fault diagnosis. Industrial Electronics, IEEE Transactions on. 2000, 47(5): 1060-1069
[34] Javier, S., Ricardo, P., Consuelo, H. Fault diagnosis of rotating machinery based on auto-associative neural networks and wavelet transforms. Journal of Sound and Vibration. 2007, 302(4-5): 981-999
[35] Uluyol, O., Kyusung, K., Nwadiogbu, E. O. Synergistic use of soft computing technologies for fault detection in gas turbine engines. Systems, Man and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on. 2006, 36(4): 476-484
[36] Wang, Z., Liu, Y., Griffin, P. J. Neural net and expert system diagnose transformer faults. Computer Applications in Power, IEEE. 2000, 13(1): 50-55
[37]虞和济,陈长征,张省等.基于神经网络的智能诊断. (第1版).北京:冶金工业出版社, 2000.
[38]沈东,褚福涛,陈思.水轮发电机组振动故障诊断与识别.水动力学研究与进展. 2000, 15(1): 129-133
[39]徐玉秀.复杂机械故障诊断的分形与小波方法. (第1版).北京:机械工业出版社, 2003.
[40]董毓新.水轮发电机组振动. (第1版).大连:大连理工大学出版社, 1989.
[41]沈东.水力机组故障分析. (第1版).北京:中国水利水电出版社, 1996.
[42]梁武科,张彦宁,罗兴锜.水电机组故障诊断系统信号特征的提取.大电机技术. 2003, 35(4): 53-56
[43]梁武科,罗兴锜,张彦宁等.水力发电机组振动故障诊断系统中的信号预处理.水力发电学报. 2003, 22(3): 114-120
[44] Wang, M. H. Application of extension theory to vibration fault diagnosis of generator sets. Generation, Transmission and Distribution, IEE Proceedings-. 2004, 151(4): 503 - 508
[45] Ogaji, S. O. T., Marinai, L., Sampath, S., et al. Gas-turbine fault diagnostics: a fuzzy-logic approach. Applied Energy. 2005, 82(1): 81-89
[46] Xiang, C., Huang, X., Huang, D., et al. Wavelets Neural Network Based on Particle Swarm Optimization Algorithm for Fault Diagnosis. in: Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on. Beijing, China: vol. 3, 2006. 320-323
[47] Hu, N.-S., He, N.-N., Hu, S. Fault diagnosis of the steam turbine condenser system based on SOM neural network. in: Machine Learning and Cybernetics, 2003 International Conference on. Xi'an, China: vol. 2, 2003. 1222 - 1225
[48] Chen, C., Mo, C. A method for intelligent fault diagnosis of rotating machinery. Digital Signal Processing. 2004, 14(3): 203-217
[49]陈林刚,韩凤琴,桂中华.基于神经网络的水电机组智能故障诊断系统.电网技术. 2006, 30(1): 40-43
[50]符向前,刘光临,蒋劲. BP神经网络在水轮发电机组状态监测与诊断系统中的应用.武汉大学学报(工学版). 2002, 35(1): 24-28
[51]傅闯,叶鲁卿,余刃等.基于多智能体和人工神经网络的水电厂预知维护系统的研究.中国电机工程学报. 2005, 25(6): 81-87
[52]姬巧玲,蔡维由,漆为民等.改进Elman网络及其在水轮发电机组故障诊断中的应用.水电自动化与大坝监测. 2005, 29(6): 31-35
[53]贾嵘,白亮,罗兴錡等.基于神经网络的水轮发电机组振动故障诊断专家系统.水力发电学报. 2004, 23(6): 120-123
[54]李辉,贾嵘,白亮.集成神经网络在水轮发电机组故障诊断中的应用.中国农村水利水电. 2005, (1): 100-102
[55]刘立峰.基于遗传神经网络和证据理论融合的水电机组振动故障诊断研究.西北水电. 2006, 25(4): 73-76
[56]彭兵,周建中,付波等.径向基神经网络在水轮发电机组故障诊断中的应用.水电自动化与大坝监测. 2006, 30(1): 35-38
[57]陈卫钢,周建中,常黎.基于专家系统的水电机组振动故障诊断研究.华中科技大学学报(自然科学版). 2002, 30(6): 102-104
[58]邓正鹏,韦彩新,刘利娜等.水电机组故障诊断专家系统中知识库的设计.华中科技大学学报(自然科学版). 2003, 31(9): 4-6
[59]余涛,王晶,沈善德等.分布式水电机组故障诊断集成专家系统的研究.动力工程. 2002, 22(4): 1902-1907
[60]陈铁华,陈启卷.模糊聚类分析在水电机组振动故障诊断中的应用.中国电机工程学报. 2002, 22(3): 43-47
[61]刘晓波,黄其柏.基于动态核聚类分析的水轮机组故障模式识别.华中科技大学学报(自然科学版). 2005, 33(9): 47-50
[62]刘晓波,黄其柏.水轮发电机组故障诊断模糊专家系统研究.华中科技大学学报(自然科学版). 2006, 34(1): 71-73
[63]程宝清,韩凤琴,桂中华.基于小波的灰色预测理论在水电机组故障预测中的应用.电网技术. 2005, 29(13): 40-44
[64]华斌,周建中,喻菁.贝叶斯网络在水电机组故障诊断中的应用研究.华北电力大学学报. 2004, 31(5): 33-36
[65]陈喜阳,张克危,彭玉成.基于多种诊断方法的水电机组故障诊断系统的研究.水力发电. 2003, 29(5): 23-26
[66] Sun, D., Huang, T.-S., Li, G., et al. The fault tendency analysis of hydro-generator based on WNN. in: Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on. Shanghai, China: vol. 5, 2004. 3090-3094
[67] Yu, X., Liao, R., Yao, C. Fault diagnosis in hydraulic turbine governor based on BPneural network. in: Electrical Machines and Systems, 2001. ICEMS 2001. Proceedings of the Fifth International Conference on. Shenyang, China: vol. 1, 2001. 335-338
[68] Xiao, Z., Guo, J., Zhao, X. Application of Fault Tree and Model Based Fault Diagnosis in Actuation System of Hydro turbine Governors. in: Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on. Dalian, China: vol. 2, 2006. 7600-7603
[69] Vapnik, V. N.统计学习理论的本质. (第1版).张学工译.北京:清华大学出版社, 2000.
[70]陶卿,姚穗,范劲松等.一种新的机器学习算法:Support Vector Machines.模式识别与人工智能. 2000, 13(3): 285-290
[71] Cherkassky, V., Mulier, F. Vapnik-chervonenkis (vc) learning theory and its applications. Neural Networks, IEEE Transactions on. 1999, 10(5): 985-987
[72] Boser, B. E., Guyon, I. M., Vapnik, V. N. A training algorithm for optimal margin classifiers. in: Proceedings of the fifth annual workshop on Computational learning theory. Pittsburgh, Pennsylvania, United States: ACM Press, 1992. 144 - 152
[73] Vapnik, V. N. An overview of statistical learning theory. Neural Networks, IEEE Transactions on. 1999, 10(5): 988-999
[74] Cortes, C., Vapnik, V. N. Support-Vector Networks. Machine Learning. 1995, 20(3): 273-297
[75] Vapnik, V. N., Golowich, S., Smola, A. J. Support vector method for function approximation, regression estimation and signal processing. in: Mozer, M. C., Jordan, M. I. and Petsche, T., editors. Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, vol. 9, 1997. 281-287
[76]卢增祥,李衍达.交互支持向量机学习算法及其应用.清华大学学报(自然科学版). 1999, 39(7): 93-97
[77] Kobayashi, K., Nakano, R. Faster optimization of SVR hyperparameters based on minimizing cross-validation error. in: Cybernetics and Intelligent Systems, 2004 IEEE Conference on. Singapore: vol. 2, 2004. 1022-1027
[78] Ning, Y., Ruixiang, S., Yingan, L., et al. Support vector machine with orthogonalChebyshev kernel. in: Pattern Recognition, 2006. ICPR 2006. 18th International Conference on. HongKong: vol. 2, 2006. 752-755
[79] Ying, T., Jun, W. A support vector machine with a hybrid kernel and minimal Vapnik-Chervonenkis dimension. Knowledge and Data Engineering, IEEE Transactions on. 2004, 16(4): 385-395
[80] Strauss, D. J., Steidl, G. Hybrid wavelet-support vector classification of waveforms. Journal of Computational and Applied Mathematics. 2002, 148(2): 375-400
[81] Amari, S., Wu, S. Improving support vector machine classifiers by modifying kernel functions. Neural Networks. 1999, 12(6): 783-789
[82] Peter, W., Sheng, L., Jianfeng, F., et al. A Geometrical Method to Improve Performance of the Support Vector Machine. Neural Networks, IEEE Transactions on. 2007, 18(3): 942
[83] Camps-Valls, G., Martin-Guerrero, J. D., Rojo-Alvarez, J. L., et al. Fuzzy sigmoid kernel for support vector classifiers. Neurocomputing. 2004, 62: 501-506
[84]褚蕾蕾,陈绥阳,周梦等.计算智能的数学基础. (第1版).北京:科学出版社, 2002.
[85] Osuna, E., Freund, R., Girosi, F. An improved training algorithm for support vector machines. in: Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop. Amelia Island, FL, USA: 1997. 276-285
[86] Osuna, E., Freund, R., Girosit, F. Training support vector machines: an application to face detection. in: Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on. San Juan, Puerto Rico: 1997. 130-136
[87] Platt, J. C. Fast training of support vector machines using sequential minimal optimization. in. Advances in kernel methods: support vector learning, MIT Press; 1999.185-208
[88] Keerthi, S. S., Shevade, S. K., Bhattacharyya, C., et al. Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Comput. 2001, 13(3): 637-649
[89] Joachims, T. Making large- scale SVM learning pratical. in: Sch olkopf, B., Burges, C. J. C. and Smola, A. J., editors. Advanced in kernel methods - support vector learning, Cambridge, USA: MIT Press; 1999.41-56
[90] Chang, C.-C., Lin, C.-J.LIBSVM: a library for support vector machines. 2001. http://www.csie.ntu.edu.tw/~cjlin/libsvm
[91] Suykens, J. A. K., Gestel, T. V., Brabanter, J. D., et al. Least Squares Support Vector Machines. Singapore: World Scientific, 2002.
[92] Glenn, F., Olvi, L. M. Proximal support vector machine classifiers. in: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. San Francisco, California: ACM Press, 2001. 77 - 86
[93] Lee, Y.-J., Mangasarian, O. L. SSVM: A Smooth Support Vector Machine for Classification. Comput. Optim. Appl. 2001, 20(1): 5-22
[94] Weston, J., Watkins, C. Support vector machines for multiclass pattern recognition. in: Proceedings of 7th Europeans Symposium on Artificical Neural Networks (ESANN'99). Bruges, Belgium: 1999. 219-224
[95] Crammer, K., Singer, Y. On the Learnability and Design of Output Codes for Multiclass Problems. Machine Learning. 2002, 47(2): 201-233
[96] Dietterich, T. G., Bakiri, G. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research. 1995, 2: 263-286
[97] U.Kreβel. Pairwise classification and support vector machines. in: Scholkopf, B., Burges, C. J. C. and Smola, A. J., editors. Advances in Kernel Methods—Support Vector Learning, Cambridge, MA: MIT Press; 1999.255-268
[98] Bottou, L., Cortes, C., Denker, J. S., et al. Comparison of classifier methods: A case study in handwritten digit recognition. in: International Conference on Pattern Recognition. Jerusalem, Isr: IEEE, Piscataway, NJ, USA, vol. 2, 1994. 77-82
[99] Platt, J. C., Cristianini, N., Shawe-Taylor, J. Large Margin DAGs for Multiclass Classification. Advances in Neural Information Processing Systems. 2000, 12: 547-553
[100] Hsu, C.-W., Lin, C.-J. A comparison of methods for multiclass support vector machines. Neural Networks, IEEE Transactions on. 2002, 13(2): 415-425
[101] Asharaf, S., Shevade, S. K., Murty, M. N. Rough support vector clustering. Pattern Recognition. 2005, 38(10): 1779-1783
[102] Li, Z., Weida, Z., Licheng, J. Hidden space support vector machines. NeuralNetworks, IEEE Transactions on. 2004, 15(6): 1424-1434
[103] Lin, C.-F., Wang, S.-D. Fuzzy support vector machines. Neural Networks, IEEE Transactions on. 2002, 13(2): 464-471
[104] Burges, C. J. C. A Tutorial on Support Vector Machines for Pattern recognition. Data Mining and Knowledge Discovery. 1998, 2(2): 121-167
[105] Gorgevik, D., Cakmakov, D. Handwritten Digit Recognition by Combining SVM Classifiers. in: Computer as a Tool, 2005. EUROCON 2005.The International Conference on. Belgrade, Serbia and Montenegro: vol. 2, 2005. 1393 -1396
[106] Smola, A. J., Scholkopf, B. A tutorial on support vector regression. Statistics And Computing. 2004, 14(3): 199-222
[107] Zheng, D., Wang, J., Zhao, Y. Non-flat function estimation with a multi-scale support vector regression. Neurocomputing. 2006, 70(1-3): 420-429
[108] Pai, P.-F., Lin, C.-S. A hybrid ARIMA and support vector machines model in stock price forecasting. Omega. 2005, 33(6): 497-505
[109] Pai, P.-F., Hong, W.-C. Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electric Power Systems Research. 2005, 74(3): 417-425
[110] Cao, D.-Z., Pang, S.-L., Bai, Y.-H. Forecasting exchange rate using support vector machines. in: Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on. Guangzhou, China: vol. 6, 2005. 3448 - 3452
[111] Mitra, V., Wang, C.-J., Banerjee, S. Text classification: A least square support vector machine approach. Applied Soft Computing Journal. 2007, 7(3): 908-914
[112] Ubeyli, E. D. ECG beats classification using multiclass support vector machines with error correcting output codes. Digital Signal Processing. 2007, 17(3): 675-684
[113] Chen, C., Tian, Y., Zou, X., et al. Prediction of protein secondary structure content using support vector machine. Talanta. 2007, 71(5): 2069-2073
[114] Seo, K.-K. An application of one-class support vector machines in content-based image retrieval. Expert Systems with Applications. 2007, 33(2): 491-498
[115] Widodo, A., Yang, B.-S. Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing. 2006:1-15.doi:10.1016/j.ymssp.2006.12.007
[116] Poyhonen, S., Negrea, M., Arkkio, A., et al. Fault diagnostics of an electrical machine with multiple support vector classifiers. in: International Symposium on Intelligent Control. Vancouver, Canada: 2002. 373-378
[117]张周锁,李凌均,何正嘉.基于支持向量机的机械故障诊断方法研究.西安交通大学学报. 2002, 36(12): 1303-1306
[118]肖健华,樊可清,吴今培等.应用于故障诊断的SVM理论研究.振动、测试与诊断. 2001, 21(4): 258-263
[119] Haykin, S. Neural Network, A Comprehensive Foundation. (第2版).叶世伟,史忠植译.北京:机械工业出版社, 2004.
[120] Cherkassky, V., Mulier, F. Learning from Data: Concepts, Theory and methods. NY: John Viley&Sons, 1997.
[121] Cristianini, N., Taylor, J. S.支持向量机导论. (第1版).李国正译.北京:电子工业出版社, 2004.
[122]张学工.关于统计学习理论与支持向量机.自动化学报. 2000, 26(1): 32-42
[123] Vladimir, V., Esther, L., Yann Le, C. Measuring the VC-dimension of a learning machine. Neural Computation. 1994, 6(5): 851-876
[124]祝海龙.统计学习理论的工程应用: [博士学位论文].西安:西安交通大学, 2002.
[125] Bartlett, P., Shawe-Taylor, J. Generalization performance of support vector machines and other pattern classifiers. in: Scholkopf, B., Burges, C. J. C. and Smola, A. J., editors. Advances in Kernel Methods - Support Vector Learning, Cambridge, USA: MIT Press; 1998.
[126] Federico, G., Michael, J., Tomaso, P. Regularization theory and neural networks architectures. Neural Comput. 1995, 7(2): 219-269
[127] Isabelle, G., Bernhard, E. B., Vladimir, V. Automatic Capacity Tuning of Very Large VC-Dimension Classifiers. in: Advances in Neural Information Processing Systems 5, [NIPS Conference]. San Mateo: Morgan Kaufmann Publishers Inc., 1993. 147-155
[128] Scholkopf, B., Mika, S., Burges, C. J. C., et al. Input space versus feature space in kernel-based methods. Neural Networks, IEEE Transactions on. 1999, 10(5): 1000-1017
[129]原峰山,朱思铭.利用傅立叶变换筛选核函数的方法.中山大学学报(自然科学版). 2004, 43(3): 10-14
[130] van Gestel, T., Suykens, J., Baesens, B., et al. Benchmarking Least Squares Support Vector Machine Classifiers. Machine Learning. 2004, 54(1): 5-32
[131] Suykens, J. A. K., Vandewalle, J. Least Squares Support Vector Machine Classifiers. Neural Processing Letters. 1999, 9(3): 293-300
[132] Suykens, J. A. K. Nonlinear modelling and support vector machines. in: Instrumentation and Measurement Technology Conference, 2001. IMTC 2001. Proceedings of the 18th IEEE. Budapest: vol. 1, 2001. 287 - 294
[133]阎威武,邵惠鹤.支持向量机和最小二乘支持向量机的比较及应用研究.控制与奖惩. 2003, 18(3): 358-360
[134] Suykens, J. A. K., De Brabanter, J., Lukas, L., et al. Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing. 2002, 48(1-4): 85-105
[135]何正友,陈小勤.基于多尺度能量统计和小波能量熵测度的电力暂态信号识别方法.中国电机工程学报. 2006, 26(10): 33-39
[136]何正嘉,訾艳阳,孟庆丰等.机械设备非平稳信号的故障诊断原理及应用. (第1版).北京:高等教育出版社, 2001.
[137] Mallat, S. G. A theory for multiresolution signal decomposition: the wavelet representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 1989, 11(7): 674 - 693
[138]程正兴.小波分析算法和应用.西安:西安交通大学出版社, 1998.
[139]刘春生,张晓春.实用小波分析. (第1版).徐州:中国矿业大学出版社, 2002.
[140]胡昌华,张军波,夏军.基于Matlab的系统分析与设计—小波分析. (第1版).西安:西安电子科技大学出版社, 1999.
[141]王海.水轮发电机组状态监测、诊断及综合试验分析系统研究: [博士学位论文].武汉:华中科技大学, 2001.
[142] Zou, M., Zhou, J., Zhang, Y., et al. Support Vector Machines based Approach for Fault Diagnosis of Hydroturbine Generating Units. in: 2007 IEEE International Conference on Control and Automation. Guangzhou, China: 2007. 152-156
[143]虞和济,韩庆大,李沈.设备故障诊断工程. (第1版).北京:冶金工业出版社, 2001.
[144] Suykens, J. A. K., Vandewalle, J. Multiclass least squares support vector machines. in: Neural Networks,1999.IJCNN'99.International Joint Conference on. Washington,DC: vol. 2, 1999. 900-903
[145] Keerthi, S. S., Lin, C. J. Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation. 2003, 15(7): 1667-1689
[146] H.T.Lin, C.J.Lin.A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods.2003.http://www.csie.ntu.edu.tw/~cjlin/papers/tanh.pdf
[147] Kim, K.-j. Financial time series forecasting using support vector machines. Neurocomputing. 2003, 55(1-2): 307-319
[148]王江萍.机械设备故障诊断技术及应用. (第1版).西安:西北工业大学出版社, 2001.
[149] Takens, F. Detecting strange attractors in turbulence. in. Dynamical Systems and Turbulence, Warwick 1980; 1981.366-381
[150]吕金虎,陆君安,陈士华.混沌时间序列分析及其应用.武汉:武汉大学出版社, 2002.
[151]刘舒,孟建.样本序列的可预测性研究.预测. 1997, (2): 47-49
[152]刘金冷.预测与决策技术. (第1版).天津:天津科学技术出版社, 1998.
[153]于海燕,张凤玲.基于模糊神经网络的电力负荷短期预测.电网技术. 2007, 31(3): 68-72
[154]王光强,周佩玲.神经网络算法在股指预测中的应用.计算机工程. 2006, 32(1): 211-212
[155] Zou, M., Zhou, J., Liu, Z., et al. A Hybrid Model for Hydroturbine Generating Unit Trend Analysis. in: The 3rd International Conference on Natural Computation (ICNC'07). HaiKou, China: VolⅡ.2007,570-574
[156] Ferreira, T. A. E., Vasconcelos, G. C., Adeodato, P. J. L. A New Evolutionary Approach for Time Series Forecasting. in: Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on. Honolulu, HI, USA: 2007.616-623
[157]马千里,郑启伦,彭宏等.基于相空间重构理论与递归神经网络相结合的股票短期预测方法.计算机应用研究. 2007, 24(4): 239-242
[158]张先起,梁川,刘慧卿.基于混沌相空间技术的输沙量预测BP网络模型.水力发电学报. 2007, 26(1): 24-27
[159] Packard, N. H., Crutchfield, J. P., Farmer, J. D., et al. Geometry from a Time Series. Physical Review Letters. 1980, 45(9): 712 - 716
[160]黄润生,黄浩.混沌及其应用. (第2版).武汉:武汉大学出版社, 2006.
[161] Cao, L. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena. 1997, 110(1-2): 43-50
[162] Kennel, M. B., Brown, R., Abarbanel, H. D. I. Determining embedding dimension for phase-space reconstruction using a geometrical construction. Physical Review A (General Physics). 1992, 46(6): 3404-3411
[163] Fraser, A. M., Swinney, H. L. Independent coordinates for strange attractors from mutual information. Physical Review A (General Physics). 1986, 33(2): 1134-1140
[164]李凌均.统计学习理论在机械设备智能诊断中的应用研究: [博士学位论文].西安:西安交通大学, 2003.
[165]邹敏,周建中,刘忠等.基于支持向量机的水电机组状态趋势预测研究.水力发电. 2007, 33(2): 63-65
[166]王小平,曹立明.遗传算法—理论、应用与软件实现. (第1版).西安:西安交通大学出版社, 2002.
[167]周明,孙树栋.遗传算法原理及应用. (第1版).北京:国防工业出版社, 1999.
[168]苑希民.神经网络和遗传算法在水科学领域的应用. (第1版).北京:中国水利水电出版社, 2002.
[169] Yang, H.-Z., Jiao, X.-N., Zhang, L.-Q., et al. Parameter Optimization for SVM using Sequential Number Theoretic for Optimization. in: Machine Learning and Cybernetics, 2006 International Conference on. Dalian,China: 2006. 3461 - 3464
[170] Liu, J., Zhang, T. A method determining parameters of SVR model based on Probability and Statistics. in: Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on. Dalian,China: vol. 1, 2006. 1553-1557
[2]梁为,李洛虎.机械故障诊断. (第1版).北京:煤炭工业出版社, 2005.
[3]赵林度.大型机电系统故障诊断技术. (第1版).北京:中国石化出版社, 2001.
[4]叶鲁卿.水力发电过程控制—理论、应用及其发展. (第1版).武汉:华中科技大学出版社, 2002.
[5] Nandi, S., Toliyat, H. A., Li, X. Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review. Energy Conversion, IEEE Transactions on. 2005, 20(4): 719-729
[6] Awadallah, M. A., Morcos, M. M. Application of AI tools in fault diagnosis of electrical machines and drives-an overview. Energy Conversion, IEEE Transaction on. 2003, 18(2): 245-251
[7] Dash, S., Venkatasubramanian, V. Challenges in the industrial applications of fault diagnostic systems. Computers & Chemical Engineering. 2000, 24(2-7): 785-791
[8]杨建东,莫剑,唐岳灏等.水电站过渡过程程序可视化设计的思路及实现.水电能源科学. 2005, 23(1): 57-59
[9]刘晓亭,刘昱.水电机组状态检修的实施及关键技术.水力发电. 2004, 30(4): 38-41
[10]何永勇,任继顺,陈伟, et al.水电机组远程状态监测、跟踪分析与故障诊断系统.清华大学学报(自然科学版). 2006, 46(5): 629-632
[11]傅闯.基于Multi-Agent的ICMMS建模及其预知维护子系统的研究: [博士学位论文].武汉:华中科技大学, 2003.
[12] Yang, S. K. A condition-based failure-prediction and processing-scheme for preventive maintenance. Reliability, IEEE Transactions on. 2003, 52(3): 373-383
[13] Durocher, D. B., Feldmeier, G. R. Predictive versus preventive maintenance. IndustryApplications Magazine, IEEE. 2004, 10(5): 12-21
[14] Fu, C., Ye, L., Liu, Y., et al. Predictive maintenance in intelligent-control-maintenance-management system for hydroelectric generating unit. Energy Conversion, IEEE Transaction on. 2004, 19(1): 179-186
[15]边肇祺,张学工.模式识别. (第2版).北京:清华大学出版社, 2000.
[16]黄文虎,夏松波.设备故障诊断原理、技术及应用. (第1版).北京:科学出版社, 1996.
[17]张来斌.机械设备故障诊断技术及方法. (第1版).北京:石油工业出版社, 2000.
[18]钟秉林,黄仁.机械故障诊断学. (第1版).北京:机械工业出版社, 1997.
[19]王道平.故障智能诊断系统的理论与方法. (第1版).北京:冶金工业出版社, 2001.
[20] Shi, W.-W., Yan, H.-S., Ma, K.-P. A new method of early fault diagnosis based on machine learning. in: Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on. Guangzhou, China: vol. 6, 2005. 3271-3276
[21] Wu, L.-H., Jiang, Y.-F., Huang, W., et al. The Intelligent Fault Diagnosis for Composite Systems Based on Machine Learning. in: Machine Learning and Cybernetics, 2006 International Conference on. Dalian, China: 2006. 571-575
[22] Murphey, Y. L., A., M. M., Chen, Z., et al. Model-based fault diagnosis in electric drives using machine learning. Mechatronics, IEEE/ASME Transactions on. 2006, 11(3): 290-303
[23] Ishigaki, T., Higuchi, T., Watanabe, K. Spectrum Classification for Early Fault Diagnosis of the LP Gas Pressure Regulator Based on the Kullback-Leibler Kernel. in: Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on. Maynooth, Ireland: 2006. 453-458
[24]吴今培,肖健华.智能故障诊断与专家系统. (第1版).北京:科学出版社, 1997.
[25]冯志鹏.计算智能在机械设备故障诊断中的应用研究: [博士学位论文].大连:大连理工大学, 2003.
[26]虞和济,侯广琳.故障诊断的专家系统. (第1版).北京:治金工业出版社, 1991.
[27] Purkait, P., Chakravorti, S. Time and frequency domain analyses based expert system for impulse fault diagnosis in transformers. Dielectrics and Electrical Insulation,IEEE Transactions on [see also Electrical Insulation, IEEE Transactions on]. 2002, 9(3): 433-445
[28] Yang, B.-S., Lim, D.-S., Tan, A. C. C. VIBEX: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table. Expert Systems with Applications. 2005, 28(4): 735-742
[29] Afgan, N. H., Carvalho, M. G., Pilavachi, P. A., et al. An expert system concept for diagnosis and monitoring of gas turbine combustion chambers. Applied Thermal Engineering. 2006, 26(7): 766-771
[30] Song, G., He, Y., Chu, F., et al. HYDES: A Web-based hydro turbine fault diagnosis system. Expert Systems with Applications. 2006, doi:10.1016/j.eswa.2006.10.017
[31] Venkat, V., King, C. A neural network methodology for process fault diagnosis. Journal of AIChE. 1989, 35(12): 1993-2002
[32] Mohamed, E. A., Abdelaziz, A. Y., Mostafa, A. S. A neural network-based scheme for fault diagnosis of power transformers. Electric Power Systems Research. 2005, 75(1): 29-39
[33] Li, B., Chow, M. Y., Tipsuwan, Y., et al. Neural-network-based motor rolling bearing fault diagnosis. Industrial Electronics, IEEE Transactions on. 2000, 47(5): 1060-1069
[34] Javier, S., Ricardo, P., Consuelo, H. Fault diagnosis of rotating machinery based on auto-associative neural networks and wavelet transforms. Journal of Sound and Vibration. 2007, 302(4-5): 981-999
[35] Uluyol, O., Kyusung, K., Nwadiogbu, E. O. Synergistic use of soft computing technologies for fault detection in gas turbine engines. Systems, Man and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on. 2006, 36(4): 476-484
[36] Wang, Z., Liu, Y., Griffin, P. J. Neural net and expert system diagnose transformer faults. Computer Applications in Power, IEEE. 2000, 13(1): 50-55
[37]虞和济,陈长征,张省等.基于神经网络的智能诊断. (第1版).北京:冶金工业出版社, 2000.
[38]沈东,褚福涛,陈思.水轮发电机组振动故障诊断与识别.水动力学研究与进展. 2000, 15(1): 129-133
[39]徐玉秀.复杂机械故障诊断的分形与小波方法. (第1版).北京:机械工业出版社, 2003.
[40]董毓新.水轮发电机组振动. (第1版).大连:大连理工大学出版社, 1989.
[41]沈东.水力机组故障分析. (第1版).北京:中国水利水电出版社, 1996.
[42]梁武科,张彦宁,罗兴锜.水电机组故障诊断系统信号特征的提取.大电机技术. 2003, 35(4): 53-56
[43]梁武科,罗兴锜,张彦宁等.水力发电机组振动故障诊断系统中的信号预处理.水力发电学报. 2003, 22(3): 114-120
[44] Wang, M. H. Application of extension theory to vibration fault diagnosis of generator sets. Generation, Transmission and Distribution, IEE Proceedings-. 2004, 151(4): 503 - 508
[45] Ogaji, S. O. T., Marinai, L., Sampath, S., et al. Gas-turbine fault diagnostics: a fuzzy-logic approach. Applied Energy. 2005, 82(1): 81-89
[46] Xiang, C., Huang, X., Huang, D., et al. Wavelets Neural Network Based on Particle Swarm Optimization Algorithm for Fault Diagnosis. in: Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on. Beijing, China: vol. 3, 2006. 320-323
[47] Hu, N.-S., He, N.-N., Hu, S. Fault diagnosis of the steam turbine condenser system based on SOM neural network. in: Machine Learning and Cybernetics, 2003 International Conference on. Xi'an, China: vol. 2, 2003. 1222 - 1225
[48] Chen, C., Mo, C. A method for intelligent fault diagnosis of rotating machinery. Digital Signal Processing. 2004, 14(3): 203-217
[49]陈林刚,韩凤琴,桂中华.基于神经网络的水电机组智能故障诊断系统.电网技术. 2006, 30(1): 40-43
[50]符向前,刘光临,蒋劲. BP神经网络在水轮发电机组状态监测与诊断系统中的应用.武汉大学学报(工学版). 2002, 35(1): 24-28
[51]傅闯,叶鲁卿,余刃等.基于多智能体和人工神经网络的水电厂预知维护系统的研究.中国电机工程学报. 2005, 25(6): 81-87
[52]姬巧玲,蔡维由,漆为民等.改进Elman网络及其在水轮发电机组故障诊断中的应用.水电自动化与大坝监测. 2005, 29(6): 31-35
[53]贾嵘,白亮,罗兴錡等.基于神经网络的水轮发电机组振动故障诊断专家系统.水力发电学报. 2004, 23(6): 120-123
[54]李辉,贾嵘,白亮.集成神经网络在水轮发电机组故障诊断中的应用.中国农村水利水电. 2005, (1): 100-102
[55]刘立峰.基于遗传神经网络和证据理论融合的水电机组振动故障诊断研究.西北水电. 2006, 25(4): 73-76
[56]彭兵,周建中,付波等.径向基神经网络在水轮发电机组故障诊断中的应用.水电自动化与大坝监测. 2006, 30(1): 35-38
[57]陈卫钢,周建中,常黎.基于专家系统的水电机组振动故障诊断研究.华中科技大学学报(自然科学版). 2002, 30(6): 102-104
[58]邓正鹏,韦彩新,刘利娜等.水电机组故障诊断专家系统中知识库的设计.华中科技大学学报(自然科学版). 2003, 31(9): 4-6
[59]余涛,王晶,沈善德等.分布式水电机组故障诊断集成专家系统的研究.动力工程. 2002, 22(4): 1902-1907
[60]陈铁华,陈启卷.模糊聚类分析在水电机组振动故障诊断中的应用.中国电机工程学报. 2002, 22(3): 43-47
[61]刘晓波,黄其柏.基于动态核聚类分析的水轮机组故障模式识别.华中科技大学学报(自然科学版). 2005, 33(9): 47-50
[62]刘晓波,黄其柏.水轮发电机组故障诊断模糊专家系统研究.华中科技大学学报(自然科学版). 2006, 34(1): 71-73
[63]程宝清,韩凤琴,桂中华.基于小波的灰色预测理论在水电机组故障预测中的应用.电网技术. 2005, 29(13): 40-44
[64]华斌,周建中,喻菁.贝叶斯网络在水电机组故障诊断中的应用研究.华北电力大学学报. 2004, 31(5): 33-36
[65]陈喜阳,张克危,彭玉成.基于多种诊断方法的水电机组故障诊断系统的研究.水力发电. 2003, 29(5): 23-26
[66] Sun, D., Huang, T.-S., Li, G., et al. The fault tendency analysis of hydro-generator based on WNN. in: Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on. Shanghai, China: vol. 5, 2004. 3090-3094
[67] Yu, X., Liao, R., Yao, C. Fault diagnosis in hydraulic turbine governor based on BPneural network. in: Electrical Machines and Systems, 2001. ICEMS 2001. Proceedings of the Fifth International Conference on. Shenyang, China: vol. 1, 2001. 335-338
[68] Xiao, Z., Guo, J., Zhao, X. Application of Fault Tree and Model Based Fault Diagnosis in Actuation System of Hydro turbine Governors. in: Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on. Dalian, China: vol. 2, 2006. 7600-7603
[69] Vapnik, V. N.统计学习理论的本质. (第1版).张学工译.北京:清华大学出版社, 2000.
[70]陶卿,姚穗,范劲松等.一种新的机器学习算法:Support Vector Machines.模式识别与人工智能. 2000, 13(3): 285-290
[71] Cherkassky, V., Mulier, F. Vapnik-chervonenkis (vc) learning theory and its applications. Neural Networks, IEEE Transactions on. 1999, 10(5): 985-987
[72] Boser, B. E., Guyon, I. M., Vapnik, V. N. A training algorithm for optimal margin classifiers. in: Proceedings of the fifth annual workshop on Computational learning theory. Pittsburgh, Pennsylvania, United States: ACM Press, 1992. 144 - 152
[73] Vapnik, V. N. An overview of statistical learning theory. Neural Networks, IEEE Transactions on. 1999, 10(5): 988-999
[74] Cortes, C., Vapnik, V. N. Support-Vector Networks. Machine Learning. 1995, 20(3): 273-297
[75] Vapnik, V. N., Golowich, S., Smola, A. J. Support vector method for function approximation, regression estimation and signal processing. in: Mozer, M. C., Jordan, M. I. and Petsche, T., editors. Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, vol. 9, 1997. 281-287
[76]卢增祥,李衍达.交互支持向量机学习算法及其应用.清华大学学报(自然科学版). 1999, 39(7): 93-97
[77] Kobayashi, K., Nakano, R. Faster optimization of SVR hyperparameters based on minimizing cross-validation error. in: Cybernetics and Intelligent Systems, 2004 IEEE Conference on. Singapore: vol. 2, 2004. 1022-1027
[78] Ning, Y., Ruixiang, S., Yingan, L., et al. Support vector machine with orthogonalChebyshev kernel. in: Pattern Recognition, 2006. ICPR 2006. 18th International Conference on. HongKong: vol. 2, 2006. 752-755
[79] Ying, T., Jun, W. A support vector machine with a hybrid kernel and minimal Vapnik-Chervonenkis dimension. Knowledge and Data Engineering, IEEE Transactions on. 2004, 16(4): 385-395
[80] Strauss, D. J., Steidl, G. Hybrid wavelet-support vector classification of waveforms. Journal of Computational and Applied Mathematics. 2002, 148(2): 375-400
[81] Amari, S., Wu, S. Improving support vector machine classifiers by modifying kernel functions. Neural Networks. 1999, 12(6): 783-789
[82] Peter, W., Sheng, L., Jianfeng, F., et al. A Geometrical Method to Improve Performance of the Support Vector Machine. Neural Networks, IEEE Transactions on. 2007, 18(3): 942
[83] Camps-Valls, G., Martin-Guerrero, J. D., Rojo-Alvarez, J. L., et al. Fuzzy sigmoid kernel for support vector classifiers. Neurocomputing. 2004, 62: 501-506
[84]褚蕾蕾,陈绥阳,周梦等.计算智能的数学基础. (第1版).北京:科学出版社, 2002.
[85] Osuna, E., Freund, R., Girosi, F. An improved training algorithm for support vector machines. in: Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop. Amelia Island, FL, USA: 1997. 276-285
[86] Osuna, E., Freund, R., Girosit, F. Training support vector machines: an application to face detection. in: Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on. San Juan, Puerto Rico: 1997. 130-136
[87] Platt, J. C. Fast training of support vector machines using sequential minimal optimization. in. Advances in kernel methods: support vector learning, MIT Press; 1999.185-208
[88] Keerthi, S. S., Shevade, S. K., Bhattacharyya, C., et al. Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Comput. 2001, 13(3): 637-649
[89] Joachims, T. Making large- scale SVM learning pratical. in: Sch olkopf, B., Burges, C. J. C. and Smola, A. J., editors. Advanced in kernel methods - support vector learning, Cambridge, USA: MIT Press; 1999.41-56
[90] Chang, C.-C., Lin, C.-J.LIBSVM: a library for support vector machines. 2001. http://www.csie.ntu.edu.tw/~cjlin/libsvm
[91] Suykens, J. A. K., Gestel, T. V., Brabanter, J. D., et al. Least Squares Support Vector Machines. Singapore: World Scientific, 2002.
[92] Glenn, F., Olvi, L. M. Proximal support vector machine classifiers. in: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. San Francisco, California: ACM Press, 2001. 77 - 86
[93] Lee, Y.-J., Mangasarian, O. L. SSVM: A Smooth Support Vector Machine for Classification. Comput. Optim. Appl. 2001, 20(1): 5-22
[94] Weston, J., Watkins, C. Support vector machines for multiclass pattern recognition. in: Proceedings of 7th Europeans Symposium on Artificical Neural Networks (ESANN'99). Bruges, Belgium: 1999. 219-224
[95] Crammer, K., Singer, Y. On the Learnability and Design of Output Codes for Multiclass Problems. Machine Learning. 2002, 47(2): 201-233
[96] Dietterich, T. G., Bakiri, G. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research. 1995, 2: 263-286
[97] U.Kreβel. Pairwise classification and support vector machines. in: Scholkopf, B., Burges, C. J. C. and Smola, A. J., editors. Advances in Kernel Methods—Support Vector Learning, Cambridge, MA: MIT Press; 1999.255-268
[98] Bottou, L., Cortes, C., Denker, J. S., et al. Comparison of classifier methods: A case study in handwritten digit recognition. in: International Conference on Pattern Recognition. Jerusalem, Isr: IEEE, Piscataway, NJ, USA, vol. 2, 1994. 77-82
[99] Platt, J. C., Cristianini, N., Shawe-Taylor, J. Large Margin DAGs for Multiclass Classification. Advances in Neural Information Processing Systems. 2000, 12: 547-553
[100] Hsu, C.-W., Lin, C.-J. A comparison of methods for multiclass support vector machines. Neural Networks, IEEE Transactions on. 2002, 13(2): 415-425
[101] Asharaf, S., Shevade, S. K., Murty, M. N. Rough support vector clustering. Pattern Recognition. 2005, 38(10): 1779-1783
[102] Li, Z., Weida, Z., Licheng, J. Hidden space support vector machines. NeuralNetworks, IEEE Transactions on. 2004, 15(6): 1424-1434
[103] Lin, C.-F., Wang, S.-D. Fuzzy support vector machines. Neural Networks, IEEE Transactions on. 2002, 13(2): 464-471
[104] Burges, C. J. C. A Tutorial on Support Vector Machines for Pattern recognition. Data Mining and Knowledge Discovery. 1998, 2(2): 121-167
[105] Gorgevik, D., Cakmakov, D. Handwritten Digit Recognition by Combining SVM Classifiers. in: Computer as a Tool, 2005. EUROCON 2005.The International Conference on. Belgrade, Serbia and Montenegro: vol. 2, 2005. 1393 -1396
[106] Smola, A. J., Scholkopf, B. A tutorial on support vector regression. Statistics And Computing. 2004, 14(3): 199-222
[107] Zheng, D., Wang, J., Zhao, Y. Non-flat function estimation with a multi-scale support vector regression. Neurocomputing. 2006, 70(1-3): 420-429
[108] Pai, P.-F., Lin, C.-S. A hybrid ARIMA and support vector machines model in stock price forecasting. Omega. 2005, 33(6): 497-505
[109] Pai, P.-F., Hong, W.-C. Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electric Power Systems Research. 2005, 74(3): 417-425
[110] Cao, D.-Z., Pang, S.-L., Bai, Y.-H. Forecasting exchange rate using support vector machines. in: Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on. Guangzhou, China: vol. 6, 2005. 3448 - 3452
[111] Mitra, V., Wang, C.-J., Banerjee, S. Text classification: A least square support vector machine approach. Applied Soft Computing Journal. 2007, 7(3): 908-914
[112] Ubeyli, E. D. ECG beats classification using multiclass support vector machines with error correcting output codes. Digital Signal Processing. 2007, 17(3): 675-684
[113] Chen, C., Tian, Y., Zou, X., et al. Prediction of protein secondary structure content using support vector machine. Talanta. 2007, 71(5): 2069-2073
[114] Seo, K.-K. An application of one-class support vector machines in content-based image retrieval. Expert Systems with Applications. 2007, 33(2): 491-498
[115] Widodo, A., Yang, B.-S. Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing. 2006:1-15.doi:10.1016/j.ymssp.2006.12.007
[116] Poyhonen, S., Negrea, M., Arkkio, A., et al. Fault diagnostics of an electrical machine with multiple support vector classifiers. in: International Symposium on Intelligent Control. Vancouver, Canada: 2002. 373-378
[117]张周锁,李凌均,何正嘉.基于支持向量机的机械故障诊断方法研究.西安交通大学学报. 2002, 36(12): 1303-1306
[118]肖健华,樊可清,吴今培等.应用于故障诊断的SVM理论研究.振动、测试与诊断. 2001, 21(4): 258-263
[119] Haykin, S. Neural Network, A Comprehensive Foundation. (第2版).叶世伟,史忠植译.北京:机械工业出版社, 2004.
[120] Cherkassky, V., Mulier, F. Learning from Data: Concepts, Theory and methods. NY: John Viley&Sons, 1997.
[121] Cristianini, N., Taylor, J. S.支持向量机导论. (第1版).李国正译.北京:电子工业出版社, 2004.
[122]张学工.关于统计学习理论与支持向量机.自动化学报. 2000, 26(1): 32-42
[123] Vladimir, V., Esther, L., Yann Le, C. Measuring the VC-dimension of a learning machine. Neural Computation. 1994, 6(5): 851-876
[124]祝海龙.统计学习理论的工程应用: [博士学位论文].西安:西安交通大学, 2002.
[125] Bartlett, P., Shawe-Taylor, J. Generalization performance of support vector machines and other pattern classifiers. in: Scholkopf, B., Burges, C. J. C. and Smola, A. J., editors. Advances in Kernel Methods - Support Vector Learning, Cambridge, USA: MIT Press; 1998.
[126] Federico, G., Michael, J., Tomaso, P. Regularization theory and neural networks architectures. Neural Comput. 1995, 7(2): 219-269
[127] Isabelle, G., Bernhard, E. B., Vladimir, V. Automatic Capacity Tuning of Very Large VC-Dimension Classifiers. in: Advances in Neural Information Processing Systems 5, [NIPS Conference]. San Mateo: Morgan Kaufmann Publishers Inc., 1993. 147-155
[128] Scholkopf, B., Mika, S., Burges, C. J. C., et al. Input space versus feature space in kernel-based methods. Neural Networks, IEEE Transactions on. 1999, 10(5): 1000-1017
[129]原峰山,朱思铭.利用傅立叶变换筛选核函数的方法.中山大学学报(自然科学版). 2004, 43(3): 10-14
[130] van Gestel, T., Suykens, J., Baesens, B., et al. Benchmarking Least Squares Support Vector Machine Classifiers. Machine Learning. 2004, 54(1): 5-32
[131] Suykens, J. A. K., Vandewalle, J. Least Squares Support Vector Machine Classifiers. Neural Processing Letters. 1999, 9(3): 293-300
[132] Suykens, J. A. K. Nonlinear modelling and support vector machines. in: Instrumentation and Measurement Technology Conference, 2001. IMTC 2001. Proceedings of the 18th IEEE. Budapest: vol. 1, 2001. 287 - 294
[133]阎威武,邵惠鹤.支持向量机和最小二乘支持向量机的比较及应用研究.控制与奖惩. 2003, 18(3): 358-360
[134] Suykens, J. A. K., De Brabanter, J., Lukas, L., et al. Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing. 2002, 48(1-4): 85-105
[135]何正友,陈小勤.基于多尺度能量统计和小波能量熵测度的电力暂态信号识别方法.中国电机工程学报. 2006, 26(10): 33-39
[136]何正嘉,訾艳阳,孟庆丰等.机械设备非平稳信号的故障诊断原理及应用. (第1版).北京:高等教育出版社, 2001.
[137] Mallat, S. G. A theory for multiresolution signal decomposition: the wavelet representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 1989, 11(7): 674 - 693
[138]程正兴.小波分析算法和应用.西安:西安交通大学出版社, 1998.
[139]刘春生,张晓春.实用小波分析. (第1版).徐州:中国矿业大学出版社, 2002.
[140]胡昌华,张军波,夏军.基于Matlab的系统分析与设计—小波分析. (第1版).西安:西安电子科技大学出版社, 1999.
[141]王海.水轮发电机组状态监测、诊断及综合试验分析系统研究: [博士学位论文].武汉:华中科技大学, 2001.
[142] Zou, M., Zhou, J., Zhang, Y., et al. Support Vector Machines based Approach for Fault Diagnosis of Hydroturbine Generating Units. in: 2007 IEEE International Conference on Control and Automation. Guangzhou, China: 2007. 152-156
[143]虞和济,韩庆大,李沈.设备故障诊断工程. (第1版).北京:冶金工业出版社, 2001.
[144] Suykens, J. A. K., Vandewalle, J. Multiclass least squares support vector machines. in: Neural Networks,1999.IJCNN'99.International Joint Conference on. Washington,DC: vol. 2, 1999. 900-903
[145] Keerthi, S. S., Lin, C. J. Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation. 2003, 15(7): 1667-1689
[146] H.T.Lin, C.J.Lin.A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods.2003.http://www.csie.ntu.edu.tw/~cjlin/papers/tanh.pdf
[147] Kim, K.-j. Financial time series forecasting using support vector machines. Neurocomputing. 2003, 55(1-2): 307-319
[148]王江萍.机械设备故障诊断技术及应用. (第1版).西安:西北工业大学出版社, 2001.
[149] Takens, F. Detecting strange attractors in turbulence. in. Dynamical Systems and Turbulence, Warwick 1980; 1981.366-381
[150]吕金虎,陆君安,陈士华.混沌时间序列分析及其应用.武汉:武汉大学出版社, 2002.
[151]刘舒,孟建.样本序列的可预测性研究.预测. 1997, (2): 47-49
[152]刘金冷.预测与决策技术. (第1版).天津:天津科学技术出版社, 1998.
[153]于海燕,张凤玲.基于模糊神经网络的电力负荷短期预测.电网技术. 2007, 31(3): 68-72
[154]王光强,周佩玲.神经网络算法在股指预测中的应用.计算机工程. 2006, 32(1): 211-212
[155] Zou, M., Zhou, J., Liu, Z., et al. A Hybrid Model for Hydroturbine Generating Unit Trend Analysis. in: The 3rd International Conference on Natural Computation (ICNC'07). HaiKou, China: VolⅡ.2007,570-574
[156] Ferreira, T. A. E., Vasconcelos, G. C., Adeodato, P. J. L. A New Evolutionary Approach for Time Series Forecasting. in: Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on. Honolulu, HI, USA: 2007.616-623
[157]马千里,郑启伦,彭宏等.基于相空间重构理论与递归神经网络相结合的股票短期预测方法.计算机应用研究. 2007, 24(4): 239-242
[158]张先起,梁川,刘慧卿.基于混沌相空间技术的输沙量预测BP网络模型.水力发电学报. 2007, 26(1): 24-27
[159] Packard, N. H., Crutchfield, J. P., Farmer, J. D., et al. Geometry from a Time Series. Physical Review Letters. 1980, 45(9): 712 - 716
[160]黄润生,黄浩.混沌及其应用. (第2版).武汉:武汉大学出版社, 2006.
[161] Cao, L. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena. 1997, 110(1-2): 43-50
[162] Kennel, M. B., Brown, R., Abarbanel, H. D. I. Determining embedding dimension for phase-space reconstruction using a geometrical construction. Physical Review A (General Physics). 1992, 46(6): 3404-3411
[163] Fraser, A. M., Swinney, H. L. Independent coordinates for strange attractors from mutual information. Physical Review A (General Physics). 1986, 33(2): 1134-1140
[164]李凌均.统计学习理论在机械设备智能诊断中的应用研究: [博士学位论文].西安:西安交通大学, 2003.
[165]邹敏,周建中,刘忠等.基于支持向量机的水电机组状态趋势预测研究.水力发电. 2007, 33(2): 63-65
[166]王小平,曹立明.遗传算法—理论、应用与软件实现. (第1版).西安:西安交通大学出版社, 2002.
[167]周明,孙树栋.遗传算法原理及应用. (第1版).北京:国防工业出版社, 1999.
[168]苑希民.神经网络和遗传算法在水科学领域的应用. (第1版).北京:中国水利水电出版社, 2002.
[169] Yang, H.-Z., Jiao, X.-N., Zhang, L.-Q., et al. Parameter Optimization for SVM using Sequential Number Theoretic for Optimization. in: Machine Learning and Cybernetics, 2006 International Conference on. Dalian,China: 2006. 3461 - 3464
[170] Liu, J., Zhang, T. A method determining parameters of SVR model based on Probability and Statistics. in: Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on. Dalian,China: vol. 1, 2006. 1553-1557
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |