基于支持向量机的机械故障模式分类研究
|
摘要
机械故障诊断本质上是一个模式分类问题。支持向量机由于解决分类问题有着优良的表现，得到日益广泛的应用。本论文针对三种现实分类问题：两类分类、单值分类和多类分类问题，基于支持向量机分类理论，结合工程实际项目对机械模式分类方法进行了研究。
论文首先介绍了机械故障诊断的历史、意义及研究现状，分析了现有故障诊断理论方法的优点及不足之处。接着，就机械故障诊断的应用背景——汽车检测领域作了叙述和总结，描述了汽车总成关键部件驱动桥试验台的开发现状。讨论了支持向量机(SVM)理论研究方法及其国内外的应用研究现状，对于故障模式分类问题，SVM也有相应理论和应用研究。论文针对解决三种机械故障模式分类的问题，提出了一个基于SVM理论的的故障诊断架构。最后，给出了本论文的主要研究内容。
第二章介绍了支持向量机的基本理论和算法。回顾了支持向量机的理论基础——统计学习理论和机器学习，讨论了统计学习理论的结构和需要进一步研究的领域，在此基础上引出支持向量机。分析了支持向量机分类理论的原理和算法，简略的介绍了基于支持向量机的两类、单值和多类分类算法，为后续章节解决实际问题作一个理论铺垫。
第三章对驱动桥故障诊断的两类分类问题进行了研究。由于故障诊断包含特征提取和诊断决策过程，根据驱动桥疲劳试验表现出的故障非线性特征，提出用核主元分析的方法首先对试验过程中采集到的正常和故障样本进行核主元特征提取，作为支持向量机的输入向量。结合核主元和支持向量机共同具有的核函数特点，提出层次向量机故障诊断方法，采用遗传算法优化层次向量机的核函数参数及和折衷参数，对驱动桥疲劳试验的正常和裂纹故障作了识别诊断，得到较满意结果。
第四章针对汽车驱动桥异响的诊断缺乏足够故障样本的问题，提出基于支持向量数据描述算法进行诊断。分别利用时域和频域指标作为支持向量数据描述分类器的输入特征向量。时域方法采用多个时域统计指标，然后用主元进行特征提取。频域方法根据正常和异响信号的能量不均衡，采用谱熵提取频域指标。两种特征提取的方法结合支持向量数据描述分类器对汽车驱动桥的异响诊断都取得
Machine fault diagnosis is a problem of pattern classification in nature. Because of the excellent performance on classification, support vector machine (SVM) is more and more widely used to solve classified problems in practical world. Based on SVM theory, this dissertation develops a research on machine fault pattern classification for engineering project applications.
The thesis first introduces the history, the significance and the current research status of machine fault diagnosis. After analyzing the virtue and shortcoming of current fault diagnosis theories, the paper summarizes the application background of machine fault diagnosis. In the field of auto detection, driving axle is a key assembly component. The development of driving axle test system is described. The SVM theory and its research status in nation and oversea is analyzed. The availability of SVM application on machine fault diagnosis is discussed. Through the above analysis, a structure of fault diagnosis based on SVM theory is founded. In the end of this chapter, the main research content and method path of this thesis is proposed.
Chapter two introduces the theory and arithmetic of SVM. After reviewing the basic theory-statistical learning theory (SLT) and machine learning (ML), the thesis discusses the SLT's structure and the question which SLT is faced. Based on analyzing the principle and arithmetic of SVM, three classification methods are introduced to present theory matting for latter chapters.
Chapter three does a research on two class classifier problem of fault diagnosis for driving axle. Feature extraction and diagnosis decision-making are two parts of fault diagnosis. Because of the non-linearity charactering of axle faulty in fatigue experimentation, the paper firstly proposes kernel primary component analysis (KPCA) method to extract the features from normal and fault samples in the course of fatigue test. Then the kernel primary components are used as the input of SVM classifier. For kernel function is used both in KPCA and SVM, the paper proposes the concept of kernel SVM and uses genetic arithmetico (GA) to optimize the compromise parameters of kernel function and SVM. Under the disassembly situation
论文首先介绍了机械故障诊断的历史、意义及研究现状，分析了现有故障诊断理论方法的优点及不足之处。接着，就机械故障诊断的应用背景——汽车检测领域作了叙述和总结，描述了汽车总成关键部件驱动桥试验台的开发现状。讨论了支持向量机(SVM)理论研究方法及其国内外的应用研究现状，对于故障模式分类问题，SVM也有相应理论和应用研究。论文针对解决三种机械故障模式分类的问题，提出了一个基于SVM理论的的故障诊断架构。最后，给出了本论文的主要研究内容。
第二章介绍了支持向量机的基本理论和算法。回顾了支持向量机的理论基础——统计学习理论和机器学习，讨论了统计学习理论的结构和需要进一步研究的领域，在此基础上引出支持向量机。分析了支持向量机分类理论的原理和算法，简略的介绍了基于支持向量机的两类、单值和多类分类算法，为后续章节解决实际问题作一个理论铺垫。
第三章对驱动桥故障诊断的两类分类问题进行了研究。由于故障诊断包含特征提取和诊断决策过程，根据驱动桥疲劳试验表现出的故障非线性特征，提出用核主元分析的方法首先对试验过程中采集到的正常和故障样本进行核主元特征提取，作为支持向量机的输入向量。结合核主元和支持向量机共同具有的核函数特点，提出层次向量机故障诊断方法，采用遗传算法优化层次向量机的核函数参数及和折衷参数，对驱动桥疲劳试验的正常和裂纹故障作了识别诊断，得到较满意结果。
第四章针对汽车驱动桥异响的诊断缺乏足够故障样本的问题，提出基于支持向量数据描述算法进行诊断。分别利用时域和频域指标作为支持向量数据描述分类器的输入特征向量。时域方法采用多个时域统计指标，然后用主元进行特征提取。频域方法根据正常和异响信号的能量不均衡，采用谱熵提取频域指标。两种特征提取的方法结合支持向量数据描述分类器对汽车驱动桥的异响诊断都取得
Machine fault diagnosis is a problem of pattern classification in nature. Because of the excellent performance on classification, support vector machine (SVM) is more and more widely used to solve classified problems in practical world. Based on SVM theory, this dissertation develops a research on machine fault pattern classification for engineering project applications.
The thesis first introduces the history, the significance and the current research status of machine fault diagnosis. After analyzing the virtue and shortcoming of current fault diagnosis theories, the paper summarizes the application background of machine fault diagnosis. In the field of auto detection, driving axle is a key assembly component. The development of driving axle test system is described. The SVM theory and its research status in nation and oversea is analyzed. The availability of SVM application on machine fault diagnosis is discussed. Through the above analysis, a structure of fault diagnosis based on SVM theory is founded. In the end of this chapter, the main research content and method path of this thesis is proposed.
Chapter two introduces the theory and arithmetic of SVM. After reviewing the basic theory-statistical learning theory (SLT) and machine learning (ML), the thesis discusses the SLT's structure and the question which SLT is faced. Based on analyzing the principle and arithmetic of SVM, three classification methods are introduced to present theory matting for latter chapters.
Chapter three does a research on two class classifier problem of fault diagnosis for driving axle. Feature extraction and diagnosis decision-making are two parts of fault diagnosis. Because of the non-linearity charactering of axle faulty in fatigue experimentation, the paper firstly proposes kernel primary component analysis (KPCA) method to extract the features from normal and fault samples in the course of fatigue test. Then the kernel primary components are used as the input of SVM classifier. For kernel function is used both in KPCA and SVM, the paper proposes the concept of kernel SVM and uses genetic arithmetico (GA) to optimize the compromise parameters of kernel function and SVM. Under the disassembly situation
引文
1．杨叔子，丁洪，史铁林．基于知识的诊断推理．北京：清华大学出版社，1992．
2．楼应候，蒋亚南．机械设备故障诊断与监测技术的发展趋势．机床与液压 2002：(4)：7-9，127．
3. Sohve J S. Trouble-shooting to stop vibration of centrifugal. Petro/Chem. Engineer 1968; (11): 22-23.
4．白木万博[日]．机械振动讲演论文集．郑州机械研究所，1984
5．张礼勇，蒋京翔等．故障诊断技术出国考察报告．机电部哈尔滨电工仪表研究所，1991．
6．高金吉．高速涡轮机械振动故障机理及诊断方法的研究．[博士论文]，1993．
7．徐敏．设备故障诊断手册．西安：西安交通大学出版社，1999．
8．史铁林，陈勇辉，李巍华等．提高大型复杂机电系统故障诊断质量的几种新方法．机械工程学报 2003；39(9)：1-10．
9．张贤达．现代信号处理．北京：清华大学出版社，1995．
10．刘振兴，尹项根，张哲等．基于瞬时功率信号频谱分析的鼠笼式异步电动机转子故障在线诊断方法．中国电机工程学报 2003：23(10)：148-152．
11．张华君，韩崇昭，王立琦等．一种基于非线性频谱分析的故障预报方法及其应用．计算机工程与应用 2003；39(14)：55-58．
12．黄建国．现代谱分析的发展及应用(一)．数据采集与处理 1990：5(4)：47-55．
13．黄建国．现代谱分析的发展及应用(二)．数据采集与处理 1991：6(1)：33-45．
14．赵守国，王素品．肺音信号非高斯ARMA模型及双谱研究．西安交通大学学报 2001：34(4)：355-359．
15. Soo-Chang Pei, etc. High resolution Wigner Distribution using Chirp Z-Transform analysis. Signal Processing 1990: p161-163.
16. Li-Chen Lin, etc. On the convergence of wavelet-based iterative signal extrapolation algorithms. Signal Processing 1996; 48: p51-65.
17. Daubechies I. The Wavelet Transform Time-Frequency Localization and Signal Analysis. IEEE Trans on Information Theory 1990; 36(5): p961-1006.
18．赵翔，萧德云．故障诊断技术的研究现状与发展趋势．机床与液压 2002：(4)：3-6，133．
19．钟佑明，秦树人．一种振动信号新变换法的研究．振动工程学报 2002：15(2)：233-238．
20．张建俊．汽车检测与故障诊断技术．北京：机械工业出版社，1999．
21．王良模，王和福．IVECO汽车驱动桥试验台的研制．南京理工大学学报：自然科学版 1997：21(2)：189-192．
22．陈朝阳，张代胜，任佩红．汽车故障诊断专家系统的现状与发展趋势．机械工程学报 2003：39(11)：1-6．
23．林军，陈庆春．驱动桥总成在线检测计算机测控系统研究．汽车工程 2002：24(1)：56-59．
24. Vapnik V. N. Estimation of Dependencies Based on Empirical Data. Berlin: Springer-Verlag, 1982.
25. Vapnik V. N. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
26. Cherkassky V, Mulier F. Learning from Data: Concepts, Theory and Methods. New York: JohnViley & Sons, 1997.
27. Tipping M. E. The relevance vector machine: MIT Press, 2000. 652-658.
28. Gestel T. Van, J. A. K. Suykens, J. De Brabanter, B. De Moor, J. Vandewalle. Kernel canonical correlation analysis and least squares support vector machines. In Proceedings of the International Conference on Artificial Neural Networks, 2001.
29. Bradley P. S, O. L. Mangasarian. Massive data discrimination via linear support vector machines: Computer Sciences Department, University of Wisconsin, Madison, Wisconsin, 1998.
30. Colin. C. Algorithmic Approaches to Training Support Vector Machines: A survey. Proceeding of ESANN2000. Belgium: D-Facto Publications, 2000. 27-35.
31. Joachims T. Making large-scale SVM Learning Practical Advances in Kernel Methods: MIT Press, 1999.
32. Keerthi S S. et al. Improvements to Platt's SMO AIgorithm fort SVM Classier Design: Dept of Mecha. and Prod. Engin. National Uni. of Singapore.
33．刘江华，程君实．支持向量机训练算法综述．信息与控制 2002：31(1)：45-50．
34. Zhang X. G. Using class center vectors to build support vector machines. In Proceedings of NNSP'99, 1999.
35. Scholkopf B. et al. Estimating the support of a high-dimensional distribution.
36. Yang M. H, Ahuja N. A. Geometric Approach to Train Support Vector Machines. In Proceedings of CVPR 2000. Hilton Head Island. 430～437.
37. Weston J, Watkins C. Multi-class Support Vector Machines, 1998.
38. Bottou L, Cortes C, Dcnkcr et al. Comparison of Classifier Methods: A Case Study in Handwriting Digit Recognition. International Conference on Pattern Recognition: IEEE Computer Society Press. 77-87.
39. Kreel U. Pairwise Classification and Support Vector Machines: The MIT Press, 1999. 255-268.
40. Platt J C, Cfisfianini N, Shawe-Taylor J. Large Margin DAGs for Multi-class Classification: MIT Press, 2000. 547-553.
41．Vapnik V．N．著．张学工译．统计学习理论的本质．北京：清华大学出版社，1999．
42．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．
43. Siolas G, Alche-Buc F. Support Vector Machines based on a semantic kernel for text categorization. Proceedings of the IEEE-INNS-ENNS International Joint Conference. 5, 2000. p205-209.
44. Brown M, LewisH. G, Gunn S. R. Linear spectral mixture models and support vector machines for remote sensing. IEEE Trans. Geoscience and Remote Sensing, 1998.
45. Melgani F, Bruzzone L. Support vector machines for classification of hyperspectral remote-sensing images. Geoscience and Remote Sensing Symposium, 2002. IGARSS'02. 2002. 1: IEEE International, 2002. p506-508.
46. L. J. Cao, K.S. Chua, W. K. Chong, H. P. Lee, Q. M. Gu. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing 2003; 55: 321-336.
47. Osuna E, Freund R. Training Support Vector Machines: an Application to Face Detection: Proc. of Computer Vision and Pattern Recognition, 1997. 130-136.
48．马勇，丁晓青．基于层次型支持向量机的人脸检测．清华大学学报(自然科学版) 2003; 43(1): 35～38.
49. Li Yongmin, Shaogang Gong, Heather Liddell. Support vector regression and classification based multi-view face detection and recognition. In IEEE International Conference on Automatic Face & Gesture Recognition. Grenoble, France. pages 300-305.
50. Fabien Cardinaux, Sebastien Marcel. Face Verification using MLP and SVM, 2002.
51．叶航军，自雪生，徐光祜．基于支持向量机的人脸姿态判定．清华大学学报(自然科学版) 2003：43(1)：67-70．
52. Deniz, O, Castrillon, M, Hernandez, M. Face recognition using independent component analysis and support vector machines. Pattern Recognition Letters 2003; 24(13):pp. 2153-2157.
53．王宏漫，欧宗瑛．采用PCA／ICA特征和SVM分类的人脸识别．计算机辅助设计与图形学学报 2003：15(4)：416-420．
54. Blanz V, B. Sch?Ikopf, H. Bulthoff, C. J. C. Burges, V. Vapnik, T. Vetter. Comparison of view-based object recognition algorithms using realistic 3D models. Artificial Neural Networks-ICANN'96. 1112: Springers Lecture Notes in Computer Science, 1996. p251-256.
55. Pontil M, Verri A. Support vector machines for 3D object recognition. Pattern Analysis and Machine Intelligence. IEEE Transactions 1998; 20(6): p637-646.
56. Roobaert D, Van Hulle. M. M. View-based 3D object recognition with support vector machines. Proceedings of the 1999 IEEE Signal Processing Society Workshop. 77-84.
57. Chakrabartty S, Singh G, Cauwenberghs G. Hybrid support vector machine/hidden Markov model approach for continuous speech recognition. Circuits and Systems. Proceedings of the 43rd IEEE Midwest Symposium on. 21, 2000. p828-831.
58．忻栋，杨莹春，吴朝晖．基于SVM／HMM混合模型的说话人确认．计算机辅助设计与图形学学报 2002：14(11)：1080～1082．
59. B. Samanta, K. R. Al-Balushi, S. A. Al-Araimi. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection Engineering. Applications of Artificial Intelligence 2003; 16: 657-665.
60．焦卫东，杨世锡，吴昭同．复合ICA-SVM机械状态模式分类．中国机械工程 2004：15．
61．翟永杰，王东风，韩璞．基于多类支持向量机的汽轮发电机组故障诊断．动力工程 2003：23(5)．
62. Rychetsky, R, Ortmann, S, Glesner, M. Support vector approaches for engine knock detection. In International Joint Conference on Neural Networks (IJCNN 99), 1999.
63. Jack, L. B, Nandi, A. K. Comparison of neural networks and support vector machines in condition monitoring applications. Proceedings of COMADEM 2000. Houston, TX, USA, 2000. pp. 721-730.
64．胡寿松，王源．基于支持向量机的非线性系统故障诊断．控制与决策 2001：16(5)．
65．董明，孟源源，徐长响等．基于支持向量机及油中溶解气体分析的大型电力变压器故障诊断模型研究．中国电机工程学报 2003；23(7)．
66．高学，金连文，尹俊勋等．一种基干支持向量机的手写汉字识别方法．电子学报 2002：30(5)：651-654．
67．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)：p93-97．
68. Siolas G, Alche-Buc F. Support Vector Machines based on a semantic kernel for text categorization. Neural Networks, 2000. IJCNN 2000. p205-209.
69. Melgani F, Bruzzone L. Support vector machines for classification of hyperspectral remote-sensing images. Geoscience and Remote Sensing Symposium, 2002. IGARSS'02. 1, 2002. p506-508.
70. MullerK R, Smola A J, Ratsch Getal. Predicting time series with support vector machines. Proc. of ICANN'97: Springer Lecture Notes in Computer Science, 1997. 999-1005.
71. Anguita D. Ridella S, Rovetta S. Circuital implementation of support vector machines. Electronics Letters 1998; 34(16): 1596-1597.
72．V N．Vapnik．著．许建华，张学工译．统计学习理论．北京：电子工业出版社，2004．
73．边肇祺，张学工等编著．模式识别(第二版)．北京：清华大学出版社，2000．
74. DAVID M. J. TAX, ROBERT P. W. DUIN. Support Vector Data Description. Pattern Recognition Letters 1999; 20(11-13): 1191-1199.
75. DAVID M. J. TAX, ROBERT P. W. DUIN. Support Vector Data Description. Machine Learning 2004; (54): 45-66.
76. Vapnik V N. Statistical learning theory. New York: John Viley & Sons, 1998.
77. Kressel U. Pairwise classification and support vector machines. Cambridge, Massachusetts: MIT Press, 1999. 255-268.
78．《汽车工程手册》编委会，主编．汽车工程手册试验篇．北京：人民交通出版社，2000．
79．肖云魁，主编．汽车故障诊断学．北京：北京理工大学出版社，2001．
80．陈进．机械设备振动监测和故展诊断．上海：上海交通大学出版社，1999．
81．罗公亮．核函数方法(下)．冶金自动化2002：26(4)：1-3，14．
82. Bernhard Scholkopf, Alexander J Smola Klaus, Robert Muller. Kernel Principal Component Analysis.
83. Choi, Sang Wooka, Lee, In-Beuma. Nonlinear dynamic process monitoring based on dynamic kernel PCA.
84. Lee, Jong-Mina, Yoo, ChangKyoob, Choi, Sang Wooka, Vanrolleghem, Peter A. b, Lee, In-Beuma. Nonlinear process monitoring using kernel principal component analysis.
85. Lee, Jong-Mina, Yoo, ChangKyoob, Lee, In-Beuma. Fault detection of batch processes using multiway kernel principal component analysis.
86. Cao, L. J. a, Chua, K. S. b, Chong, W. K. c, Lee, H. P. a, Gu, Q. M. d. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine.
87．罗公亮．核函数方法(上)．冶金自动化2002：26(3)：1-4．
88. Michalewicz, Z. Genetic Algorithms+Data Structures=Evolution Programs. New York, USA: Springer, 1999.
89．杨行峻，郑君里主编．人工神经网络与盲信号处理．北京：清华大学出版社，2003．
90. L. B. Jack, A. K. Nandi. Fault detection using support vector machines artificial neural networks augmented by genetic algorithms. Mechanical Systems and Signal Processing 2002; 16(2-3): 373-390.
91. B. Samanta, K. R. Al-Balushi, S. A. Al-Araimi. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence 2003; (16): 657-665.
92．李良敏，屈梁生．基于遗传编程和支持向量机的故障诊断模型．西安交通大学学报 2004：38(3)：239-242．
93．李凌均，张周锁，何正嘉．基于支持向量数据描述的机械故障诊断研究．西安交通大学学报 2003：37(9)：910～913．
94．顿德安．汽车驱动桥常见故障分析．中南汽车运输 2000：(4)：18-19．
95．梁国臣．汽车驱动桥发响原因分析．汽车技术 1996：(1)：21-30．
96．刘红星，左洪福，姜澄宇，屈梁生．信号频谱的二维向量及其应用．中国机械工程 1999：10(5)：537～539．
97．Simon Haykin．神经网络原理原书第2版．北京：机械工业出版社，2004．
98．Richard O．Duda，Peter E．Hart，David G．Stork．模式分类(第二版)．北京：机械工业出版社，2003．
99．胡劲松．面向旋转机械故障诊断的经验模态分解时频分析方法及实验研究．(博士论文)：浙江大学：2003
100．潘明清，周晓军，雷良育等．驱动桥综合性能试验台计算机控制系统的研制．组合机床与自动化加工技术 2003；(6)：49-50．
101．赵明岩．驱动桥疲劳试验技术研究及驱动桥试验台软件开发．(硕士论文)：浙江大学：2003．
102．姚卫星．结构疲劳寿命分析．北京：国防工业出版社，2003．
103．高镇同，熊峻江．疲劳可靠性．北京：北京航空航天大学出版社，2000．
104．郑金学．面向在线检测的汽车驱动桥状态监测和故障诊断技术研究及系统开发．(硕士论文)：浙江大学：2004．
105．陈予恕．非线性振动分叉和混沌理论及其应用．振动工程学报．1992，5(3)：235-250
106. Mallat S, Zhang S. Characterization of signals from multiscale edges[J]. IEEE Trans on Pattern Analysis and Machine Intelligence. 1992, 14(7): 710-732.
107. Mallat S, Hwang W L. Singularity detection and processing with wavelet[J]. IEEE Trams on Information Theory, 1992. 38(2): 617-643.
108．崔锦泰，程正兴．小波分析导论[M]．西安：西安交通大学出版社．1995．
109．杨福生．小波变换的工程分析与应用[M]．北京：科学出版社，1999．
110. Grossman A, Morlet J. Decomposition of hardy function into square integrable wavelets of constant shape[J]. SIAM J Math Anal,1984, 15: 723—736.
111．秦宣云，卜英勇．基于小波变换的故障信号检测．中南工业大学学报．2002，33(4)：434-437
112. Huang N E, Shen Z, Long S. R.. et al. The empirical mode decomposition and the Hiblert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A, 1998, 454: 903-995
113．杨世锡．基于神经网络的大型旋转机械故障诊断专家系统研究．[博士学位论文]，浙江大学．1997．
114．王忠健，苏利敏．基于遗传算法的网络故障诊断专家系统的设计与实现．北京理工大学学报 2005：25(1)：38-40，44．
115. Su Limin, Hou Chaozhen, Dai Zhongjian. et al. The application of improved genetic algorithm in network fault diagnosis expert system[J]. Journal of Beijing Institute of Technology. 2003; 12(3): 225-229.
116．史永胜，宋云雪．基于遗传算法与BP神经网络的故障诊断模型．计算机工程 2004：30(14)：125-127．
117．腾益登，曾良才，吴海峰，王巧云．基于RBF神经网络的结晶器激振在线监测与故障诊断软件模块．机床与液压 2004：(8)：185-186
118．焦李成．神经网路系统理论[J]西安：西安电子科技太学出版社，1998
119．徐波，于劲松，李行善．复杂系统的智能故障诊断．信息与控制 2004：33(1)：56-60．
120．沈艳霞，纪志成，姜建国．电机故障诊断的人工智能方法综述．微特电机 2004：32(2)：39-42．
121. Dashi M, et al. Feature selection for classification. Intelligent Data Analysis, 1997, 1: 131
122．项新建．基于粗糙集理论的变压器故障诊断专家系统研究．仪器仪表学报 2005：26(1)：93-96．
123．杜卫锋，孙士保．模糊粗糙集的表示定理．西南交通大学学报 2005：40(1)：118-121．
124．詹艳梅，曾向阳，孙进才．基于粗糙集理论的目标特征选择方法．自然科学进展 2004：14(12)：1483-1487．
125. Fenton W G. McGinnity T M, Maguire I. P. Fault diagnosis of electronic systems using intelligent techniques: a review[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Renews, 2001. 31(3): 269-281.
126. Sorsa T, Koivo H N, Koivisto H. Neural networks in process fault diagnosis[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1991; 21(4): 815-824.
127. Tang T H, Lin X, Li J R, et al. A new fuzzy neural network for approach for intelligent monitoring system[A]. Proceedings of IFAC Transportation Systems Conference[C], 1997: 691-696.
128．刘永阔，夏虹，谢春丽等．基于模糊神经网络的核动力装置设备故障诊断系统研究．核动力工程2004：25(4)：328-331．
129．段江涛，李凌均，张周锁等．基于支持向量机的机械系统多故障分类方法．农业机械学报 2004；35(4)：144-147．
130．朱永生．支持向量机及其在机械故障模式识别中的应用．(博士学位论文)．西安交通大学，2003．
131．马笑潇，黄席樾，柴毅．基于SVM的二叉树多类分类算法及其在故障诊断中的应用．控制与决策2003：18(3)：272-276，284．
132．王成栋，朱永生，张优云等．时频分析与支持向量机在柴油机气阀故障诊断中的应用．内燃机学报 2004：22(3)：245-251．
133．翟永杰，毛继佩，于丽敏，刘长良．分级聚类支持向量机在汽轮机故障诊断中的应用．华北电力大学学报 2003：30(6)：25-29．
134．许德昌，袁胜发．基于支持向量机的离心泵故障诊断方法研究．液压与气动 2004：(12)：64-65．
135. L. M. Manevitz and M. Yousef, "One-class SVMs for document classification", Journal of Machine Learning Research, vol. 2, pp. 139-154, 2001.
136. K. Worden, "Structural fault detection using a novelty measure", Journal of Sound and Vibration, vol. 201, issue 1, pp. 85-101, 1997.
137. A. Ypma and R. P. W. Duin, "Novelty detection using self-organising maps", Progress in Connectionist Based Information Systems, vol. 2, pp. 1322-1325, 1998.
138. A. Scholkopf, R. Williamson, A. Smola, J.S. Taylor and J. Platt, "Support vector method for novelty detection", In Neural Information Processing Systems, S. A. Solla, T. K. Leen and K. R. Muller (eds.), pp. 582-588, 2000.
139. C. Surace, K. Worden and G. Tomlinson, "A novelty detection approach to diagnose damage in a cracked beam", Proc. of SPIE, vol. 3089, pp. 947-953, 1997.
140. D. M. J. Tax and R. P. W. Duin, "Data domain description using support vectors", Proc. ESAN99, Brussels, pp. 251-256, 1999a.
141．焦卫东．基于独立分量分析的机器健康状况监诊方法研究．(博士论文)：浙江大学：2003
142．刘志刚，李德仁，秦前清等．支持向量机在多类分类问题中的推广．计算机工程与应用 2004：40(7)：10-13，65．
143. Chih-Wei Hsu, Chih-Jen Lin. A Comparison of Methods for Multiclass Support Vector Machines. IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 2, MARCH 2002
144．王鹏，朱小燕．基于RBF核的SVM的模型选择及其应用．计算机工程与应用 2003：39(24)：72-73．
145．高鹰，谢胜利．免疫粒子群优化算法．计算机工程与应用 2004：40(6)：4-6，33．
146．高鹰，谢胜利．基于模拟退火的粒子群优化算法．计算机工程与应用 2004：40(1)：47-50．
147．李爱国，覃征等．粒子群优化算法．计算机工程与应用2002：38(21)：1-3，17．
148．吴瑞明，周晓军，雷良育．车桥检测中的虚拟仪器技术研究．组合机床与自动化加工技术 2003：(10)：55-56．
149．吴瑞明，周晓军，赵明岩等．汽车驱动桥的疲劳检测分析．汽车工程 2003：25(3)：283-286．
2．楼应候，蒋亚南．机械设备故障诊断与监测技术的发展趋势．机床与液压 2002：(4)：7-9，127．
3. Sohve J S. Trouble-shooting to stop vibration of centrifugal. Petro/Chem. Engineer 1968; (11): 22-23.
4．白木万博[日]．机械振动讲演论文集．郑州机械研究所，1984
5．张礼勇，蒋京翔等．故障诊断技术出国考察报告．机电部哈尔滨电工仪表研究所，1991．
6．高金吉．高速涡轮机械振动故障机理及诊断方法的研究．[博士论文]，1993．
7．徐敏．设备故障诊断手册．西安：西安交通大学出版社，1999．
8．史铁林，陈勇辉，李巍华等．提高大型复杂机电系统故障诊断质量的几种新方法．机械工程学报 2003；39(9)：1-10．
9．张贤达．现代信号处理．北京：清华大学出版社，1995．
10．刘振兴，尹项根，张哲等．基于瞬时功率信号频谱分析的鼠笼式异步电动机转子故障在线诊断方法．中国电机工程学报 2003：23(10)：148-152．
11．张华君，韩崇昭，王立琦等．一种基于非线性频谱分析的故障预报方法及其应用．计算机工程与应用 2003；39(14)：55-58．
12．黄建国．现代谱分析的发展及应用(一)．数据采集与处理 1990：5(4)：47-55．
13．黄建国．现代谱分析的发展及应用(二)．数据采集与处理 1991：6(1)：33-45．
14．赵守国，王素品．肺音信号非高斯ARMA模型及双谱研究．西安交通大学学报 2001：34(4)：355-359．
15. Soo-Chang Pei, etc. High resolution Wigner Distribution using Chirp Z-Transform analysis. Signal Processing 1990: p161-163.
16. Li-Chen Lin, etc. On the convergence of wavelet-based iterative signal extrapolation algorithms. Signal Processing 1996; 48: p51-65.
17. Daubechies I. The Wavelet Transform Time-Frequency Localization and Signal Analysis. IEEE Trans on Information Theory 1990; 36(5): p961-1006.
18．赵翔，萧德云．故障诊断技术的研究现状与发展趋势．机床与液压 2002：(4)：3-6，133．
19．钟佑明，秦树人．一种振动信号新变换法的研究．振动工程学报 2002：15(2)：233-238．
20．张建俊．汽车检测与故障诊断技术．北京：机械工业出版社，1999．
21．王良模，王和福．IVECO汽车驱动桥试验台的研制．南京理工大学学报：自然科学版 1997：21(2)：189-192．
22．陈朝阳，张代胜，任佩红．汽车故障诊断专家系统的现状与发展趋势．机械工程学报 2003：39(11)：1-6．
23．林军，陈庆春．驱动桥总成在线检测计算机测控系统研究．汽车工程 2002：24(1)：56-59．
24. Vapnik V. N. Estimation of Dependencies Based on Empirical Data. Berlin: Springer-Verlag, 1982.
25. Vapnik V. N. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
26. Cherkassky V, Mulier F. Learning from Data: Concepts, Theory and Methods. New York: JohnViley & Sons, 1997.
27. Tipping M. E. The relevance vector machine: MIT Press, 2000. 652-658.
28. Gestel T. Van, J. A. K. Suykens, J. De Brabanter, B. De Moor, J. Vandewalle. Kernel canonical correlation analysis and least squares support vector machines. In Proceedings of the International Conference on Artificial Neural Networks, 2001.
29. Bradley P. S, O. L. Mangasarian. Massive data discrimination via linear support vector machines: Computer Sciences Department, University of Wisconsin, Madison, Wisconsin, 1998.
30. Colin. C. Algorithmic Approaches to Training Support Vector Machines: A survey. Proceeding of ESANN2000. Belgium: D-Facto Publications, 2000. 27-35.
31. Joachims T. Making large-scale SVM Learning Practical Advances in Kernel Methods: MIT Press, 1999.
32. Keerthi S S. et al. Improvements to Platt's SMO AIgorithm fort SVM Classier Design: Dept of Mecha. and Prod. Engin. National Uni. of Singapore.
33．刘江华，程君实．支持向量机训练算法综述．信息与控制 2002：31(1)：45-50．
34. Zhang X. G. Using class center vectors to build support vector machines. In Proceedings of NNSP'99, 1999.
35. Scholkopf B. et al. Estimating the support of a high-dimensional distribution.
36. Yang M. H, Ahuja N. A. Geometric Approach to Train Support Vector Machines. In Proceedings of CVPR 2000. Hilton Head Island. 430～437.
37. Weston J, Watkins C. Multi-class Support Vector Machines, 1998.
38. Bottou L, Cortes C, Dcnkcr et al. Comparison of Classifier Methods: A Case Study in Handwriting Digit Recognition. International Conference on Pattern Recognition: IEEE Computer Society Press. 77-87.
39. Kreel U. Pairwise Classification and Support Vector Machines: The MIT Press, 1999. 255-268.
40. Platt J C, Cfisfianini N, Shawe-Taylor J. Large Margin DAGs for Multi-class Classification: MIT Press, 2000. 547-553.
41．Vapnik V．N．著．张学工译．统计学习理论的本质．北京：清华大学出版社，1999．
42．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．
43. Siolas G, Alche-Buc F. Support Vector Machines based on a semantic kernel for text categorization. Proceedings of the IEEE-INNS-ENNS International Joint Conference. 5, 2000. p205-209.
44. Brown M, LewisH. G, Gunn S. R. Linear spectral mixture models and support vector machines for remote sensing. IEEE Trans. Geoscience and Remote Sensing, 1998.
45. Melgani F, Bruzzone L. Support vector machines for classification of hyperspectral remote-sensing images. Geoscience and Remote Sensing Symposium, 2002. IGARSS'02. 2002. 1: IEEE International, 2002. p506-508.
46. L. J. Cao, K.S. Chua, W. K. Chong, H. P. Lee, Q. M. Gu. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing 2003; 55: 321-336.
47. Osuna E, Freund R. Training Support Vector Machines: an Application to Face Detection: Proc. of Computer Vision and Pattern Recognition, 1997. 130-136.
48．马勇，丁晓青．基于层次型支持向量机的人脸检测．清华大学学报(自然科学版) 2003; 43(1): 35～38.
49. Li Yongmin, Shaogang Gong, Heather Liddell. Support vector regression and classification based multi-view face detection and recognition. In IEEE International Conference on Automatic Face & Gesture Recognition. Grenoble, France. pages 300-305.
50. Fabien Cardinaux, Sebastien Marcel. Face Verification using MLP and SVM, 2002.
51．叶航军，自雪生，徐光祜．基于支持向量机的人脸姿态判定．清华大学学报(自然科学版) 2003：43(1)：67-70．
52. Deniz, O, Castrillon, M, Hernandez, M. Face recognition using independent component analysis and support vector machines. Pattern Recognition Letters 2003; 24(13):pp. 2153-2157.
53．王宏漫，欧宗瑛．采用PCA／ICA特征和SVM分类的人脸识别．计算机辅助设计与图形学学报 2003：15(4)：416-420．
54. Blanz V, B. Sch?Ikopf, H. Bulthoff, C. J. C. Burges, V. Vapnik, T. Vetter. Comparison of view-based object recognition algorithms using realistic 3D models. Artificial Neural Networks-ICANN'96. 1112: Springers Lecture Notes in Computer Science, 1996. p251-256.
55. Pontil M, Verri A. Support vector machines for 3D object recognition. Pattern Analysis and Machine Intelligence. IEEE Transactions 1998; 20(6): p637-646.
56. Roobaert D, Van Hulle. M. M. View-based 3D object recognition with support vector machines. Proceedings of the 1999 IEEE Signal Processing Society Workshop. 77-84.
57. Chakrabartty S, Singh G, Cauwenberghs G. Hybrid support vector machine/hidden Markov model approach for continuous speech recognition. Circuits and Systems. Proceedings of the 43rd IEEE Midwest Symposium on. 21, 2000. p828-831.
58．忻栋，杨莹春，吴朝晖．基于SVM／HMM混合模型的说话人确认．计算机辅助设计与图形学学报 2002：14(11)：1080～1082．
59. B. Samanta, K. R. Al-Balushi, S. A. Al-Araimi. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection Engineering. Applications of Artificial Intelligence 2003; 16: 657-665.
60．焦卫东，杨世锡，吴昭同．复合ICA-SVM机械状态模式分类．中国机械工程 2004：15．
61．翟永杰，王东风，韩璞．基于多类支持向量机的汽轮发电机组故障诊断．动力工程 2003：23(5)．
62. Rychetsky, R, Ortmann, S, Glesner, M. Support vector approaches for engine knock detection. In International Joint Conference on Neural Networks (IJCNN 99), 1999.
63. Jack, L. B, Nandi, A. K. Comparison of neural networks and support vector machines in condition monitoring applications. Proceedings of COMADEM 2000. Houston, TX, USA, 2000. pp. 721-730.
64．胡寿松，王源．基于支持向量机的非线性系统故障诊断．控制与决策 2001：16(5)．
65．董明，孟源源，徐长响等．基于支持向量机及油中溶解气体分析的大型电力变压器故障诊断模型研究．中国电机工程学报 2003；23(7)．
66．高学，金连文，尹俊勋等．一种基干支持向量机的手写汉字识别方法．电子学报 2002：30(5)：651-654．
67．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)：p93-97．
68. Siolas G, Alche-Buc F. Support Vector Machines based on a semantic kernel for text categorization. Neural Networks, 2000. IJCNN 2000. p205-209.
69. Melgani F, Bruzzone L. Support vector machines for classification of hyperspectral remote-sensing images. Geoscience and Remote Sensing Symposium, 2002. IGARSS'02. 1, 2002. p506-508.
70. MullerK R, Smola A J, Ratsch Getal. Predicting time series with support vector machines. Proc. of ICANN'97: Springer Lecture Notes in Computer Science, 1997. 999-1005.
71. Anguita D. Ridella S, Rovetta S. Circuital implementation of support vector machines. Electronics Letters 1998; 34(16): 1596-1597.
72．V N．Vapnik．著．许建华，张学工译．统计学习理论．北京：电子工业出版社，2004．
73．边肇祺，张学工等编著．模式识别(第二版)．北京：清华大学出版社，2000．
74. DAVID M. J. TAX, ROBERT P. W. DUIN. Support Vector Data Description. Pattern Recognition Letters 1999; 20(11-13): 1191-1199.
75. DAVID M. J. TAX, ROBERT P. W. DUIN. Support Vector Data Description. Machine Learning 2004; (54): 45-66.
76. Vapnik V N. Statistical learning theory. New York: John Viley & Sons, 1998.
77. Kressel U. Pairwise classification and support vector machines. Cambridge, Massachusetts: MIT Press, 1999. 255-268.
78．《汽车工程手册》编委会，主编．汽车工程手册试验篇．北京：人民交通出版社，2000．
79．肖云魁，主编．汽车故障诊断学．北京：北京理工大学出版社，2001．
80．陈进．机械设备振动监测和故展诊断．上海：上海交通大学出版社，1999．
81．罗公亮．核函数方法(下)．冶金自动化2002：26(4)：1-3，14．
82. Bernhard Scholkopf, Alexander J Smola Klaus, Robert Muller. Kernel Principal Component Analysis.
83. Choi, Sang Wooka, Lee, In-Beuma. Nonlinear dynamic process monitoring based on dynamic kernel PCA.
84. Lee, Jong-Mina, Yoo, ChangKyoob, Choi, Sang Wooka, Vanrolleghem, Peter A. b, Lee, In-Beuma. Nonlinear process monitoring using kernel principal component analysis.
85. Lee, Jong-Mina, Yoo, ChangKyoob, Lee, In-Beuma. Fault detection of batch processes using multiway kernel principal component analysis.
86. Cao, L. J. a, Chua, K. S. b, Chong, W. K. c, Lee, H. P. a, Gu, Q. M. d. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine.
87．罗公亮．核函数方法(上)．冶金自动化2002：26(3)：1-4．
88. Michalewicz, Z. Genetic Algorithms+Data Structures=Evolution Programs. New York, USA: Springer, 1999.
89．杨行峻，郑君里主编．人工神经网络与盲信号处理．北京：清华大学出版社，2003．
90. L. B. Jack, A. K. Nandi. Fault detection using support vector machines artificial neural networks augmented by genetic algorithms. Mechanical Systems and Signal Processing 2002; 16(2-3): 373-390.
91. B. Samanta, K. R. Al-Balushi, S. A. Al-Araimi. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence 2003; (16): 657-665.
92．李良敏，屈梁生．基于遗传编程和支持向量机的故障诊断模型．西安交通大学学报 2004：38(3)：239-242．
93．李凌均，张周锁，何正嘉．基于支持向量数据描述的机械故障诊断研究．西安交通大学学报 2003：37(9)：910～913．
94．顿德安．汽车驱动桥常见故障分析．中南汽车运输 2000：(4)：18-19．
95．梁国臣．汽车驱动桥发响原因分析．汽车技术 1996：(1)：21-30．
96．刘红星，左洪福，姜澄宇，屈梁生．信号频谱的二维向量及其应用．中国机械工程 1999：10(5)：537～539．
97．Simon Haykin．神经网络原理原书第2版．北京：机械工业出版社，2004．
98．Richard O．Duda，Peter E．Hart，David G．Stork．模式分类(第二版)．北京：机械工业出版社，2003．
99．胡劲松．面向旋转机械故障诊断的经验模态分解时频分析方法及实验研究．(博士论文)：浙江大学：2003
100．潘明清，周晓军，雷良育等．驱动桥综合性能试验台计算机控制系统的研制．组合机床与自动化加工技术 2003；(6)：49-50．
101．赵明岩．驱动桥疲劳试验技术研究及驱动桥试验台软件开发．(硕士论文)：浙江大学：2003．
102．姚卫星．结构疲劳寿命分析．北京：国防工业出版社，2003．
103．高镇同，熊峻江．疲劳可靠性．北京：北京航空航天大学出版社，2000．
104．郑金学．面向在线检测的汽车驱动桥状态监测和故障诊断技术研究及系统开发．(硕士论文)：浙江大学：2004．
105．陈予恕．非线性振动分叉和混沌理论及其应用．振动工程学报．1992，5(3)：235-250
106. Mallat S, Zhang S. Characterization of signals from multiscale edges[J]. IEEE Trans on Pattern Analysis and Machine Intelligence. 1992, 14(7): 710-732.
107. Mallat S, Hwang W L. Singularity detection and processing with wavelet[J]. IEEE Trams on Information Theory, 1992. 38(2): 617-643.
108．崔锦泰，程正兴．小波分析导论[M]．西安：西安交通大学出版社．1995．
109．杨福生．小波变换的工程分析与应用[M]．北京：科学出版社，1999．
110. Grossman A, Morlet J. Decomposition of hardy function into square integrable wavelets of constant shape[J]. SIAM J Math Anal,1984, 15: 723—736.
111．秦宣云，卜英勇．基于小波变换的故障信号检测．中南工业大学学报．2002，33(4)：434-437
112. Huang N E, Shen Z, Long S. R.. et al. The empirical mode decomposition and the Hiblert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A, 1998, 454: 903-995
113．杨世锡．基于神经网络的大型旋转机械故障诊断专家系统研究．[博士学位论文]，浙江大学．1997．
114．王忠健，苏利敏．基于遗传算法的网络故障诊断专家系统的设计与实现．北京理工大学学报 2005：25(1)：38-40，44．
115. Su Limin, Hou Chaozhen, Dai Zhongjian. et al. The application of improved genetic algorithm in network fault diagnosis expert system[J]. Journal of Beijing Institute of Technology. 2003; 12(3): 225-229.
116．史永胜，宋云雪．基于遗传算法与BP神经网络的故障诊断模型．计算机工程 2004：30(14)：125-127．
117．腾益登，曾良才，吴海峰，王巧云．基于RBF神经网络的结晶器激振在线监测与故障诊断软件模块．机床与液压 2004：(8)：185-186
118．焦李成．神经网路系统理论[J]西安：西安电子科技太学出版社，1998
119．徐波，于劲松，李行善．复杂系统的智能故障诊断．信息与控制 2004：33(1)：56-60．
120．沈艳霞，纪志成，姜建国．电机故障诊断的人工智能方法综述．微特电机 2004：32(2)：39-42．
121. Dashi M, et al. Feature selection for classification. Intelligent Data Analysis, 1997, 1: 131
122．项新建．基于粗糙集理论的变压器故障诊断专家系统研究．仪器仪表学报 2005：26(1)：93-96．
123．杜卫锋，孙士保．模糊粗糙集的表示定理．西南交通大学学报 2005：40(1)：118-121．
124．詹艳梅，曾向阳，孙进才．基于粗糙集理论的目标特征选择方法．自然科学进展 2004：14(12)：1483-1487．
125. Fenton W G. McGinnity T M, Maguire I. P. Fault diagnosis of electronic systems using intelligent techniques: a review[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Renews, 2001. 31(3): 269-281.
126. Sorsa T, Koivo H N, Koivisto H. Neural networks in process fault diagnosis[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1991; 21(4): 815-824.
127. Tang T H, Lin X, Li J R, et al. A new fuzzy neural network for approach for intelligent monitoring system[A]. Proceedings of IFAC Transportation Systems Conference[C], 1997: 691-696.
128．刘永阔，夏虹，谢春丽等．基于模糊神经网络的核动力装置设备故障诊断系统研究．核动力工程2004：25(4)：328-331．
129．段江涛，李凌均，张周锁等．基于支持向量机的机械系统多故障分类方法．农业机械学报 2004；35(4)：144-147．
130．朱永生．支持向量机及其在机械故障模式识别中的应用．(博士学位论文)．西安交通大学，2003．
131．马笑潇，黄席樾，柴毅．基于SVM的二叉树多类分类算法及其在故障诊断中的应用．控制与决策2003：18(3)：272-276，284．
132．王成栋，朱永生，张优云等．时频分析与支持向量机在柴油机气阀故障诊断中的应用．内燃机学报 2004：22(3)：245-251．
133．翟永杰，毛继佩，于丽敏，刘长良．分级聚类支持向量机在汽轮机故障诊断中的应用．华北电力大学学报 2003：30(6)：25-29．
134．许德昌，袁胜发．基于支持向量机的离心泵故障诊断方法研究．液压与气动 2004：(12)：64-65．
135. L. M. Manevitz and M. Yousef, "One-class SVMs for document classification", Journal of Machine Learning Research, vol. 2, pp. 139-154, 2001.
136. K. Worden, "Structural fault detection using a novelty measure", Journal of Sound and Vibration, vol. 201, issue 1, pp. 85-101, 1997.
137. A. Ypma and R. P. W. Duin, "Novelty detection using self-organising maps", Progress in Connectionist Based Information Systems, vol. 2, pp. 1322-1325, 1998.
138. A. Scholkopf, R. Williamson, A. Smola, J.S. Taylor and J. Platt, "Support vector method for novelty detection", In Neural Information Processing Systems, S. A. Solla, T. K. Leen and K. R. Muller (eds.), pp. 582-588, 2000.
139. C. Surace, K. Worden and G. Tomlinson, "A novelty detection approach to diagnose damage in a cracked beam", Proc. of SPIE, vol. 3089, pp. 947-953, 1997.
140. D. M. J. Tax and R. P. W. Duin, "Data domain description using support vectors", Proc. ESAN99, Brussels, pp. 251-256, 1999a.
141．焦卫东．基于独立分量分析的机器健康状况监诊方法研究．(博士论文)：浙江大学：2003
142．刘志刚，李德仁，秦前清等．支持向量机在多类分类问题中的推广．计算机工程与应用 2004：40(7)：10-13，65．
143. Chih-Wei Hsu, Chih-Jen Lin. A Comparison of Methods for Multiclass Support Vector Machines. IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 2, MARCH 2002
144．王鹏，朱小燕．基于RBF核的SVM的模型选择及其应用．计算机工程与应用 2003：39(24)：72-73．
145．高鹰，谢胜利．免疫粒子群优化算法．计算机工程与应用 2004：40(6)：4-6，33．
146．高鹰，谢胜利．基于模拟退火的粒子群优化算法．计算机工程与应用 2004：40(1)：47-50．
147．李爱国，覃征等．粒子群优化算法．计算机工程与应用2002：38(21)：1-3，17．
148．吴瑞明，周晓军，雷良育．车桥检测中的虚拟仪器技术研究．组合机床与自动化加工技术 2003：(10)：55-56．
149．吴瑞明，周晓军，赵明岩等．汽车驱动桥的疲劳检测分析．汽车工程 2003：25(3)：283-286．