HMM动态模式识别理论、方法以及在旋转机械故障诊断中的应用
|
摘要
本文以国家自然科学基金项目“基于隐Markov模型的旋转机械故障诊断新方法的研究”(编号：50075079)为基础，提出的博士学位论文题目为“HMM动态模式识别理论、方法以及在旋转机械故障诊断中的应用”。本文以大型旋转机械为研究对象，研究了HMM动态模式识别理论与方法在旋转机械故障诊断中的应用，开辟了旋转机械计算机辅助故障诊断的新途径。全文主要研究内容如下：
第一章：介绍了旋转机械振动监测和诊断的概况；综述了多变量动态模式识别理论的发展和研究现状；结合国家自然科学基金提出了本文的研究内容；最后，给出了本文的总体框架和创新之处。
第二章：介绍了Markov链基本理论，并通过一个简单的实例把它扩展到了隐Markov模型(HMM)；然后重点介绍离散HMM的基本理论、算法以及在实际应用中的改进措施。
第三章：根据离散HMM(DHMM)的基本理论，提出了旋转机械振动幅值谱矢量的标量量化方法，并在此基础上提出了基于DHMM的故障诊断方法；利用转子升速过程的振动模式验证了DHMM故障诊断方法的有效性。
第四章：在连续隐Markov模型(CHMM)的基本理论基础上，提出了直接利用振动信号AR系数特征矢量序列建立混合密度CHMM的故障诊断新方法，并对转子升速过程的振动模式进行了成功的识别；对DHMM和CHMM故障诊断方法进行了对比分析，指出DHMM方法具有算法稳定、计算速度快、识别精度高等特点，对于CHMM方法只要通过合理选择特征参数也能得到高的识别精度。
第五章：利用SOM神经网络对多传感器的振动信息特征进行降维、压缩与编码，首次提出了基于多通道振动信息融合的HMM故障诊断方法，拓展了监测对象的观测范围，从而能够对旋转机械的整体运行状态做出综合识别，并对提出的方法进行了实验研究。
第六章：首次把AR系数矢量引入到Kalman滤波器和HMM中，得到了两种自适应描述非平稳动态时间序列的在线模型—Kalman-AR和HMM-AR模型；描述连续状态变化的Kalman-AR滤波模型为描述离散状态变化的HMM-AR模型提供了一个良好的初始化方法和给出过程状态转移点的先验信息，从而使HMM-AR模型能够对非平稳过程进行状态分割和分类；仿真和实验结果表明，提出的该旋转机械运行状态在线监测方法，能够实现对旋转机械运行的状态进行成功地分割和分类。
第七章：基于多变量动态模式识别的理论和方法，在混合编程环境下开发了HMM
的旋转机械故障诊断应用软件;介绍了软件系统的开发环境、开发工具以及Matlab和
C++混合编程的接口实现方法;介绍了整个软件系统的基本组成和功能。
第八章:给出了全文研究内容的总结;并对HMM理论在旋转机械故障诊断方面
的进一步应用提出了展望。
Based on the "Application on Faults Diagnosis of Rotating Machine in Hidden Markov Models" (National Nature Science Fund Project, No: 50075079), the Hidden Markov Models (HMMs) dynamic pattern recognition theories and methods are studied, then proposed the applications in faults diagnosis of rotating machine by HMM methods and developed the faults diagnosis software based on HMM. The details are studied as follows:
Chapter one briefly introduces the general situation of vibration monitoring and faults diagnosis of the rotating machine. The developing and the current situations of the multivariate dynamic pattern recognition theories are summarized. At last, the background, main contents, general structure scheme and innovation points of this dissertation are present.
Chapter two introduces the basic ideas of Markov Chain theories briefly, and then extends it to Hidden Markov Models through a simple example. At last the theories and algorithms of Hidden Markov Models are studied. The modification algorithms of HMM are proposed. Therefore the basic theories of this dissertation are established.
Chapter three proposes the basic concept of dynamical pattern recognition and introduces the implementation theories based on probability statistics. Based on the theory of the Discrete Hidden Markov Models (DHMM), the scalar quantization method of vibration spectrum vector of rotating machine is proposed, and then the fault diagnosis method based on DHMM is designed. The experiment of run-up process of rotor machine is made to verify the effect of the new faults diagnosis method.
Chapter four introduces the basic theories of Continue Hidden Markov Models (CHMM). the new method of faults diagnosis based mixture density CHMMs directly by the vibration AR coefficients vectors of rotating machine is proposed, and then the dynamic patterns presented in run-up process of rotor machine are successfully recognized. At last compares the two faults diagnosis methods of DHMM and CHMM, and points out the advantages and disadvantages of the two methods.
Chapter five studies the new method for faults diagnosis of rotating machine based on SOM and HMM through the integrated information of multi-sensor. First self organization clustering method for high dimension data is proposed, and then DHMM for faults diagnosis is designed by the low dimension features. Experiments verified that the method is effective.
Chapter six propose a useful method based on HMM-AR for modelling the dynamic
time series of rotating machine running process. It is verified through simulating data and experiments data.
Chapter seven builds the HMM faults diagnosis software for rotating machine. The developing environments, tools and implementation methods of interface between Matlab and C++ mixture programming languages on this software are introduced. At last the basic components and functions of the software are illustrated.
At last, in the eighth chapter, all of the work in this dissertation is summed up, and the future researches on applications of HMMs are prospected.
第一章：介绍了旋转机械振动监测和诊断的概况；综述了多变量动态模式识别理论的发展和研究现状；结合国家自然科学基金提出了本文的研究内容；最后，给出了本文的总体框架和创新之处。
第二章：介绍了Markov链基本理论，并通过一个简单的实例把它扩展到了隐Markov模型(HMM)；然后重点介绍离散HMM的基本理论、算法以及在实际应用中的改进措施。
第三章：根据离散HMM(DHMM)的基本理论，提出了旋转机械振动幅值谱矢量的标量量化方法，并在此基础上提出了基于DHMM的故障诊断方法；利用转子升速过程的振动模式验证了DHMM故障诊断方法的有效性。
第四章：在连续隐Markov模型(CHMM)的基本理论基础上，提出了直接利用振动信号AR系数特征矢量序列建立混合密度CHMM的故障诊断新方法，并对转子升速过程的振动模式进行了成功的识别；对DHMM和CHMM故障诊断方法进行了对比分析，指出DHMM方法具有算法稳定、计算速度快、识别精度高等特点，对于CHMM方法只要通过合理选择特征参数也能得到高的识别精度。
第五章：利用SOM神经网络对多传感器的振动信息特征进行降维、压缩与编码，首次提出了基于多通道振动信息融合的HMM故障诊断方法，拓展了监测对象的观测范围，从而能够对旋转机械的整体运行状态做出综合识别，并对提出的方法进行了实验研究。
第六章：首次把AR系数矢量引入到Kalman滤波器和HMM中，得到了两种自适应描述非平稳动态时间序列的在线模型—Kalman-AR和HMM-AR模型；描述连续状态变化的Kalman-AR滤波模型为描述离散状态变化的HMM-AR模型提供了一个良好的初始化方法和给出过程状态转移点的先验信息，从而使HMM-AR模型能够对非平稳过程进行状态分割和分类；仿真和实验结果表明，提出的该旋转机械运行状态在线监测方法，能够实现对旋转机械运行的状态进行成功地分割和分类。
第七章：基于多变量动态模式识别的理论和方法，在混合编程环境下开发了HMM
的旋转机械故障诊断应用软件;介绍了软件系统的开发环境、开发工具以及Matlab和
C++混合编程的接口实现方法;介绍了整个软件系统的基本组成和功能。
第八章:给出了全文研究内容的总结;并对HMM理论在旋转机械故障诊断方面
的进一步应用提出了展望。
Based on the "Application on Faults Diagnosis of Rotating Machine in Hidden Markov Models" (National Nature Science Fund Project, No: 50075079), the Hidden Markov Models (HMMs) dynamic pattern recognition theories and methods are studied, then proposed the applications in faults diagnosis of rotating machine by HMM methods and developed the faults diagnosis software based on HMM. The details are studied as follows:
Chapter one briefly introduces the general situation of vibration monitoring and faults diagnosis of the rotating machine. The developing and the current situations of the multivariate dynamic pattern recognition theories are summarized. At last, the background, main contents, general structure scheme and innovation points of this dissertation are present.
Chapter two introduces the basic ideas of Markov Chain theories briefly, and then extends it to Hidden Markov Models through a simple example. At last the theories and algorithms of Hidden Markov Models are studied. The modification algorithms of HMM are proposed. Therefore the basic theories of this dissertation are established.
Chapter three proposes the basic concept of dynamical pattern recognition and introduces the implementation theories based on probability statistics. Based on the theory of the Discrete Hidden Markov Models (DHMM), the scalar quantization method of vibration spectrum vector of rotating machine is proposed, and then the fault diagnosis method based on DHMM is designed. The experiment of run-up process of rotor machine is made to verify the effect of the new faults diagnosis method.
Chapter four introduces the basic theories of Continue Hidden Markov Models (CHMM). the new method of faults diagnosis based mixture density CHMMs directly by the vibration AR coefficients vectors of rotating machine is proposed, and then the dynamic patterns presented in run-up process of rotor machine are successfully recognized. At last compares the two faults diagnosis methods of DHMM and CHMM, and points out the advantages and disadvantages of the two methods.
Chapter five studies the new method for faults diagnosis of rotating machine based on SOM and HMM through the integrated information of multi-sensor. First self organization clustering method for high dimension data is proposed, and then DHMM for faults diagnosis is designed by the low dimension features. Experiments verified that the method is effective.
Chapter six propose a useful method based on HMM-AR for modelling the dynamic
time series of rotating machine running process. It is verified through simulating data and experiments data.
Chapter seven builds the HMM faults diagnosis software for rotating machine. The developing environments, tools and implementation methods of interface between Matlab and C++ mixture programming languages on this software are introduced. At last the basic components and functions of the software are illustrated.
At last, in the eighth chapter, all of the work in this dissertation is summed up, and the future researches on applications of HMMs are prospected.
引文
[1] 张正松，傅尚新，冯冠平，徐玉铮等编著．旋转机械振动监测及故障诊断．北京：机械工业出版社，1991．
[2] 刘雄，赵振毅，屈梁生编著．转子监测和诊断系统．西安：西安交通大学出版社，1991．
[3] 吴今培，肖建华著．智能故障诊断与专家系统．北京：科学出版社，1997．
[4] 程耀东编著．机械振动学．杭州：浙江大学出版社，1988．
[5] 杨叔子等．时间序列分析的工程应用．武汉：华中理工大学出版社，1991．
[6] 张贤达．时间序列分析—高阶统计量方法．北京：清华大学出版社，1996．
[7] 雷继尧，丁康编著．轴承故障诊断．西安：西安交通大学出版社，1991．
[8] Sohre J S. Trouble-shooting to stop vibration of centrifugal. Petro/Chem engineering, 1968, 11: 22-33.
[9] 白木万博阳[日]．机械振动讲演论文集．郑州机械研究所．1984．
[10] 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．
[11] 汪慰军．分岔与混沌理论在大型旋转机械非线性故障诊断中的应用研究．[博士学位论文]，浙江大学，杭州．1998．
[12] 曾复．裂纹转子非线性振动机理及实验研究．[博士学位论文]．浙江大学，杭州，2000．
[13] 黄文虎等．机械故障的数学方法．第三届全国振动理论及应用学术会议论文．1987．
[14] 夏松波 张礼勇．旋转机械故障诊断技术的现状与展望．振动与冲击．1997，16(2)：1-5．
[15] 冯志鹏，谢宇等．旋转机械振动故障诊断理论与技术进展综述．振动与冲击．2001，20(4)：36-39．
[16] J.D. Stuart, et al. Turbo Mac-An expert system to aid in the diagnosis of cause of vibration producing problems in large turbo machinery. Proc. of ASME Computer in Eng. Cant Boston in USA. 1985, 217-319.
[17] M. F. White, et al. Expert system based diagnosis of turbo machinery. Proc. of IMEKO, 5th Int Symptom Technical Diagnosis, WG. 1987: 421-429.
[18] 徐敏．欧美大型旋转机械监测诊断技术发展近况与评述．第四届全国机械设备故嚎诊断学术会议论文集．杭州：1994：3-9．
[19] 徐世吕，王日新，刘占生等．“八五”国家重点科技项目验收报告，哈尔滨电工仪表研究所，1996(3)．
[20] 刘荣强，夏松波等．汽轮机发电机组轴系稳定性的诊断方法，汽轮机技术，1994(12)．
[21] 杨叔子等．机械设备的诊断理论、技术与方法．振动工程学报．1992，5：193—201．
[22] 钟一锷等编．转子动力学．北京：清华大学出版社，1987．
[23] 吴今培编著．模糊诊断理论及其应用．北京：科学出版社，1995．
[24] 沈清、胡德文、时春编著．神经网络应用技术．长沙：国防科技大学出版社，1993．
[25] 张孝令，刘福升等编著．贝叶斯动态模型及其预测．济南：山东科学技术出版社，1992．
[26] A General Architecture for Supervised Classification of Multivariate Time Series. http://www.cse.unsw.edu.au/～waleed/phd/tr9806/tr.html.
[27] Alexander Ypma, Co Melissant, Ole Baunbaek-Jensen, Robert P. W. Duin. Health Monitoring with Learning Methods. ICANN 2001: 547-553.
[28] David M. J. Tax, Alexander Ypma, Robert P. W. Duin. Pump Failure Detection Using Support Vector Data Descriptions. IDA 1999:415-426.
[29] 谢锦辉．隐Markov模型(HMM)及其在语音处理中的应用．武汉：华中理工大学出版社，1995．
[30] Geman S, Geman D. Stochastic Relaxation, Gibbs Distribution, and the Bayesian Restoration of Images. IEEE Trans. PAMI, 1984, 6(5).
[31] 钱敏平，龚光鲁．从数学角度看计算智能．科学通报，1998，43(16)：1681-1792．
[32] Li，CJ，邱晓明．模式识别分析在线检测轴承局部缺陷．国外轴承．1990，(6)：41-47，39．
[33] 余国安，彭勇．应用模式识别技术实现抽油机井下故障的自动诊断．西安石油学院学报．1990，5(1)：23-33．
[34] Aitkin M. WiLson G. Mixture models, outline and the EM algorithm. Techno metrics, 1980,22(3):325～331
[35] Qing Wang, Zheru Chi, Feng, D.D., Rongchum Zhao. Hidden Markov random field based approach for off-line handwritten Chinese character recognition. Pattern Recognition, 2000. Proceedings. 15th International Conference on, 2000, 2:347-350.
[36] 陈锡先，王立健．多讲话者汉语单字识别．北京邮电学院学报．1989，12(2)：1-7．
[37] 徐文盛，戴蓓倩等．一种抗噪孤立字语音识别模型．中国科学技术大学学报．2000，30(6)：659-665．
[38] 方绍武，戴蓓倩．一种离散隐Markov模型参数的全局优化算法．电路与系统学报．2000，5(3)：78-81．
[39] 童进，吴昭同等．数字离散信号滤器HMM综合平均法的研究．浙江大学学报，工学版．2000，34(3)：-312-316．
[40] 郭庆，柴海新．隐Markov模型中状态停留时间的模型化．清华大学学报：自科版．1999，39(5)：97-100．
[41] 童进，吴昭同等．大型旋转机械升降速过程故障诊断研究．振动测试与诊断．1999，19(3)：193-195．
[42] 童进 吴昭同等．大型旋转机械升降速过程故障诊断HMM方法研究．机械科学与技术(西安)．1999，18(6)：973-975．
[43] 童进 吴昭同等．大型旋转机械升降速过程故障诊断HMM-AR方法研究．振动与冲击．1999，18(2)：79-80．
[44] 周德全，刘国岁．基于隐Markov模型的雷达目标识别技术研究．南京理工大学学报．1998，22(3)：224-227，251．
[45] 程俊，易克初．一种基于小波变换和隐Markov模型的声调识别方法．电子科学学刊．1997．19(2)：177-182．
[46] 高雨清，陈永彬．隐Markov模型参数估计的一种新方法．自动化学报．1991，17(1)：56-62．
[47] 方高林，高文等．基于SOFM／HMM模型的非特定人手语识别系统．计算机学报．2002，25(1)：16-21．
[48] 姚鸿勋，高文等．基于色度分析的唇动特性提取与识别．电子学报．2002，30(2)：168-172．
[49] 茅晓泉，胡光锐等．一种DHMM的混合训练方法．电子学报．2002，30(1)：148-150．
[50] 谢贤亚，李焕润等．EM—HMM多频率线跟踪算法．电子学报．2002，30(1)：22-26
[51] 刘鸣，戴蓓清等．鲁棒性话者辨识中的一种改进的马尔科夫模型．电子学报．2002，30(1)：46-48．
[52] 方绍武，戴蓓倩等．一种具有强分类能力的离散HMM训练算法．软件学报．2001，012(010)：1540-1543
[53] 李灿伟，杨震．基于HMM的说话人确认系统的研究．南京邮电学院学报：自然科学版．2001，021(002)：52-55，70
[54] 茅晓泉，胡光锐．基于最大互信息的离散隐马尔柯夫模型训练方法．上海交通大学学报．2001，035(011)：1713—1716．
[55] 赵力，邹采荣等．基于PCANN／HMM混合结构的语音识别方法．信号处理．2001，017(005)：473-476
[56] 冯长建，丁启全等．SOFM和HMM在旋转机械升降速全过程故障诊断中的应用．上海海运学院学报．2001，022(003)：98-100，104．
[57] 李士进，杨静宇等．基于奇异值特征和隐马尔可夫模型的人脸检测．中国图像图形学报．2001，6(7)：681—688．
[58] 陈友仁，赵正校．基于隐马尔可夫模型的车牌自动识别技术．红外与激光工程．2001，30(2)：102-107．
[59] 李姣军．小波变换和HMM模型在语音识别中的应用．重庆大学学报：自然科学版．2001，24(4)：61-63
[60] 赵力，刘怡龙等．基于VQ—HMM的无教师说话人自适应方法．东南大学学报．2001，31(2)：23-26
[61] 王春立，高文．具有不同数目状态结点的HMMs在中国手语识别中的应用．计算机研究与发展．2001．38(1)：-111-115．
[62] 王春启，汪鸿振．基于HMM的瞬态回波分类研究．声学技术．2000，19(1)：6-8．
[63] 何强，张有为．多观察序列连续隐含马尔柯夫模型的无溢出参数重估．电子学报．2000，28(10)：98-101．
[64] 秦姣华，向旭宇．HMM在汉字识别技术中的应用．现代计算机：下半月刊．2000，(97)：29-31．
[65] 邓伟，赵劳椿．隐马尔可夫模型的一种有区分力的反向传播训练方法．自动化学报．2000，26(4)：492-498．
[66] 姚丹霖，陈火旺．多链ANN-HMM识别模型及其应用．模式识别与人工智能．2000，13(1)：1—6．
[67] 张杰，黄志同．语音识别中隐马尔可夫模型状态数的选取原则及研究．计算机工程与应用．2000，36(1)：67-69，133．
[68] 马晓旭，张传利．基于离散隐马尔科夫模型的变压器励磁涌流鉴别新算法．电力系统自动化．2000，24(3)：23-28．
[69] 贺前华，韦岗．基于遗传算法的HMM最小错误率训练方法．电路与系统学报．1999，4(4)：46-50
[70] Heck, L.P. Signal processing research in automatic tool wear monitoring. Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on, 1993, 1:55-58.
[71] Heck, L.P., McClellan, J.H. Mechanical system monitoring using hidden Markov models, coustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on, 1991, 3:1697-1700.
[72] Smyth, P. Hidden Markov models and neural networks for fault detection in dynamic systems. Neural Networks for Processing [1993] Ⅲ. Proceedings of the 1993 IEEE-SP Workshop, 1993:582-592.
[73] Ertunc, H.M., Loparo, K.A., Ozdemir, E., Ocak, H. Real time monitoring of tool wear using multiple modeling method. Electric Machines and Drives Conference, 2000. IEMDC 2001. IEEE International, 2001:687-691.
[74] Hatzipantelis, E., Penman, J. The use of hidden Markov models for condition monitoring electrical machines. Electrical Machines and Drives, 1993. Sixth International Conference on, 1993:91-96.
[75] Ocak, H., Loparo, K.A. new bearing fault detection and diagnosis scheme based on hidden Markov modeling of vibration signals. Acoustics, Speech, and Signal Processing, 2001. Proceedings. 2001 IEEE International Conference on, 2001, 5:3141-3144.
[76] Atlas, L., Ostendorf, M., Bernard, G.D. Hidden Markov models for monitoring machining tool-wear. Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on, 2000, 6:3887-3890.
[77] Owsley, L.M.D., Atlas, L.E.; Bernard, G.D. Automatic clustering of vector time-series for
manufacturing machine monitoring. Acoustics, Speech, and Signal Processing, 1997． ICASSP-97． , 1997 IEEE International Conference on , 1997, 4: 3393-3396．
[78] Youmin Zhang, Li, X.R., Kemin Zhou. A fault detection and diagnosis approach based on hidden Markov chain model. American Control Conference, 1998． Proceedings of the 1998, 4: 2012-2016．
[79] Loparo, K.A., Adams, M.L., Lin, W., Abdel-Magied, M.F., Afshari, N. Fault detection and diagnosis of rotating machinery. Industrial Electronics, IEEE Transactions on , 2000,47(5) : 1005-1014．
[80] Wattar, I., Hafez, W., Gao, Z. Model-based diagnosis of chaotic vibration signals. Industrial Electronics Society, 1999． IECON '99 Proceedings. The 25th Annual Conference of the IEEE, 1999, 3:1177-1180．
[81] Stegemann, D., Reimche, W., Sudmersen, U., Pietsch, O., Liu, Y. Monitoring and vibrational diagnostic of rotating machinery in power plants. Power Station Maintenance-Profitability Through Reliability, 1998． First IBE/IMechE International Conference on (Conf. Publ. No. 452) , 1998: 39-44．
[82] Abdel-Magied, M.F., Loparo, K.A., Wei Lin. Fault detection and diagnosis for rotating machinery: a model-based approach. American Control Conference, 1998． Proceedings of the 1998, 6: 3291-3296．
[83] McCormick, A.C., Nandi, A.K. A comparison of artificial neural networks and other statistical methods for rotating machine condition classification. Modeling and Signal Processing for Fault Diagnosis (Digest No.: 1996/260) , IEE Colloquium on , 1996, 2: 1-6．
[84] Tanaka, M., Sakawa, M., Shiromaru, I., Matsumoto, T. Application of Kohonen's self-organizing network to the diagnosis system for rotating machinery. Systems, Man and Cybernetics, 1995． Intelligent Systems for the 21st Century., IEEE International Conference on, 1995, 5: 4039-4044．
[85] Barker, R.W., Hinich, M.J. Statistical monitoring of rotating machinery by cumulate spectral analysis. Higher-Order Statistics, 1993． , IEEE Signal Processing Workshop on, 1993: 187-191．
[86] Gaunard, P., Mubikangiey, C.G, Couvreur, C., Fontaine, V. Automatic classification of environmental noise events by hidden Markov models. Acoustics, Speech and Signal Processing, 1998． Proceedings of the 1998 IEEE International Conference on, 1998, 6: 3609-3612．
[87] Winger, L.L. Linearly constrained generalized Lloyd algorithm for reduced codebook vector quantization. Signal Processing, IEEE Transactions on, 2001, 49(7) : 1501-1509．
[88] Gader, P.D., Mystkowski, M., Yunxin Zhao. Landmine detection with ground penetrating radar using hidden Markov models. Geosciences and Remote Sensing, IEEE Transactions on, 2001, 39(6) : 1231-1244．
[89] Hasegawa, T., Yasuda, H., Matsumoto, T. Fast discrete HMM algorithm for online handwriting
recognition. Pattern Recognition, 2000． Proceedings. 15th International Conference on, 2000,4: 535-538．
[90] Jaehak Chung, Powers, E.J., Grady, W.M., Bhatt, S.C. Electric power transient disturbance classification using wavelet-based hidden Markov models. Acoustics, Speech, and Signal Processing, 2000． ICASSP '00． Proceedings. 2000 IEEE International Conference on, 2000,6: 3662-3665．
[91] Qiang Hue; Smith, N.; Bin Ma. Efficient ML training of CDHMM parameters based on prior evolution, posterior intervention and feedback. Acoustics, Speech, and Signal Processing, 2000． ICASSP '00． Proceedings. 2000 IEEE International Conference on, 2000, 2: 1001-1004．
[92] Runkle, P., Carin, L., Lam Nguyen. Multi-aspect target classification using hidden Markov models for data fusion. Geosciences and Remote Sensing Symposium, 2000． Proceedings. IGARSS 2000, 5: 2123-2125．
[93] Zhao, Y. Spectrum estimation of HMM signal source in channel distortion and additive noise. Signal Processing Proceedings, 2000． WCCC-ICSP 2000． 5th International Conference on, 2000, 1: 226-229．
[94] Chen P, Kobayashi, H. Semi-blind block channel estimation and signal detection using hidden Markov models. Global Telecommunications Conference, 2000． GLOBECOM '00． IEEE, 2000, 2: 1051-1055．
[95] Azzouzi, M., Nabney, I.T. Modeling financial time series with switching state space models. Computational Intelligence for Financial Engineering, 1999． (CIFEr) Proceedings of the IEEE/IAFE 1999 Conference on, 1999: 240-249．
[96] Liehr, S., Pawelzik, K., Kohlmorgen, J., Lemm, S., Muller, K.R. Hidden Markov mixtures of experts for prediction of non-stationary dynamics. Neural Networks for Signal Processing Ⅸ, 1999． Proceedings of the 1999 IEEE Signal Processing Society Workshop, 1999: 195-204．
[97] Matrouf, D., Gauvain, J.L. Using AR HMM state-dependent filtering for speech enhancement. Acoustics, Speech, and Signal Processing, 1999． Proceedings. 1999 IEEE International Conference on, 1999,2:785-788．
[98] Fang Sun, Guangrui Hu. Speech recognition based on genetic algorithm for training HMM. Electronics Letters, 1998,34(16) : 1563-1564．
[99] L.R. Rabiner. A tutorial on Hidden Markov Models and selected application in speech recognition. Proc. IEEE, 1989, 77(2) : 257-286．
[100] Bahl L R, Brown P F, De Souza P V, et al. A New Algorithm for the Estimation of Hidden Markov Models Parameters. Proc. ICASSP'88,1988: 493-496．
[101] Bahl L R, Jelinek F. Decoding for Channels with Insertions, Deletions, and Substitution with
Applications to Speech Recognition .IEEE Trans. IT. 1975,21(2) : 401-411．
[102] Baker J K. The DRAGON System-An Overview. IEEE Trans. ASSP. 1975,23(1) : 24-29．
[103] Nadas A, et al. On a Model-Robust Training Method for Speech Recognition. IEEE Trains. ASSP, 1988,36(9) : 1432-1436．
[104] Rabiner L R, Juang B H. An Introduction to Hidden Markov Models. IEEE ASSP Magazine. 1986, 3(1) : 4-6．
[105] Rabiner L R, et al. On the Use of Hidden Markov Models for Speaker Independent Recognition of Isolated Word from a Medium-Size Vocabulary. AT&T Bell Tab. Tech. J. 1984, 63(4) : 627-642．
[106] Bahl L R, Brown P F, et al. Maximum Mutual Information Estimation of Hidden Markov Parameters for Speech Recognition. Proc. ICASSP'86,1986: 49-52．
[107] Normandin Y, Morgan S D. An Improved MMIE Training Algorithm for Speaker-Independent, Small Vocabulary, Continuous Speech Recognition. Proc. ICASSP'91,1991: 537-540．
[108] Juang B H, Rabiner L R. The Segmental k-Means Algorithm for Estimating Parameters of Hidden Markov Models. IEEE Trans. ASSP, 1990, 38(9) : 1639-1641．
[109] Young S J. Competitive Training in Hidden Markov Models. Proc. ICASSP'88,1988: 28-31．
[110] J Pardey, S Roberts, et al. A Review of Parametric Modeling Techniques for EEG Analysis. Med. Eng. Phys., 1996,18(1) : 2-11．
[111] D W Skagen. Estimation of running frequency spectra using a Kalman filtering algorithm. Journal of Biomedical Engineering, 1988,10: 275-279．
[112] R J Meinhold, N D Singpurwalla. Understanding the Kalman Filter. The American Statistician, 1983, 37(2) : 123-127．
[113] A H Jazwinski. Adaptive filtering. Automation, 1969, 5:475-485．
[114] C M Bishop. Neural networks of pattern recognition. Oxford University Press, Oxford, 1995．
[115] Ephraim Y, Dembo A, Rabiner L R. A Minimum Discrimination Information Approach for Hidden Markov Modeling. Proc. ICASSP'87, 1987: 25-28．
[116] Ephraim Y, Rabiner L R. On the Relation between Modeling Approaches for Information Source. Proc. ICASSP'88, 1988: 24-27．
[117] Merialdo B. Phonetic Recognition Using Hidden Markov Models and Maximum Mutual Information Training. Proc. ICASSP'88,1988: 111-114．
[118] White, L.B. A comparison between optimal and filtering for hidden Markov processes. IEEE Signal Processing Letters, 1998, 5(5) : 124-126．
[119] http://www.ee.ic.ac.uk/hp/stafT/dmb/voicebox/voicebox.html.
[120] Cheng, W.T., Chan, K.L. Classification of electrocardiogram using hidden Markov models.
Engineering in Medicine and Biology Society, 1998． Proceedings of the 20th Annual International Conference of the IEEE, 1998,1: 143-146．
[121] Sahinci, E., Kamen, E.W. Detection of discrete faults in electronics assembly. Control Applications, 1998． Proceedings of the 1998 IEEE International Conference on, 1998, 2: 776-780．
[122] Azzouzi, M., Nabney, I.T. Analyzing time series structure with hidden Markov models. Neural Networks for Signal Processing Ⅷ, 1998． Proceedings of the 1998 IEEE Signal Processing Society Workshop, 1998:402-408．
[123] Logan, B., Moreno, P. Factorial HMMs for acoustic modeling. Acoustics, Speech and Signal Processing, 1998． Proceedings of the 1998 IEEE International Conference on, 1998,2: 813-816．
[124] Owsley, L.M.D., Atlas, L.E., Bernard, GD. Self-organizing feature maps and hidden Markov models for machine-tool monitoring. Signal Processing, IEEE Transactions on, 1997,45(11) : 2787-2798．
[125] Kwong, S., Chau, C.W. Analysis of parallel genetic algorithms on HMM based speech recognition system. Consumer Electronics, IEEE Transactions on, 1997,43(4) : 1229-1233．
[126] Kundu, A., Chen, QC. An integrated hybrid neural network and hidden Markov model classifier for sonar signals. Signal Processing, IEEE Transactions on, 1997,45(10) : 2566-2570．
[127] Stein, D.W.J., Dillard, GM. Applying hidden Markov models to radar detection in clutter. Radar 97 (Conf. Publ. No. 449) , 1997: 586-590．
[128] Chen, B., Willett, P. Quickest detection of hidden Markov models. Decision and Control, 1997, Proceedings of the 36th IEEE Conference on, 1997,4: 3984-3989．
[129] Kottke, D.P., Jong-Kae Fwu; Brown, K. Hidden Markov modeling for automatic target recognition. Signals, Systems & Computers, 1997． Conference Record of the Thirty-First Asilomar Conference on, 1997,1:859-863．
[130] Ramachandrula, S., Thippur, S. Scalar quantization of features in discrete hidden Markov models. Information, Communications and Signal Processing, 1997． ICICS. Proceedings of 1997 International Conference on, 1997,1: 537-540．
[131] Owsley, L.M.D., Atlas, L.E., Bernard, GD. Automatic clustering of vector time-series for manufacturing machine monitoring. Acoustics, Speech, and Signal Processing, 1997． ICASSP-97． , 1997 IEEE International Conference on, 1997,4: 3393-3396．
[132] Bengio, Y., Frasconi, P. Input-output HMMs for sequence processing. Neural Networks, IEEE Transactions on, 1996, 7(5) : 1231-1249．
[133] Ruey S. Huang, Chung J. Kuo, Ling-Ling Tsai, Chen, O.T.C. EEG pattern recognition-arousal states detection and classification. Neural Networks, 1996． IEEE International Conference on, 1996, 2: 641-646．
[134] Guoliang Fan, Xiang-Gen Xia. mage denoising using a local contextual hidden Markov model in the wavelet domain. IEEE Signal Processing Letters, 2001, 8(5): 125-128.
[135] Siohan, O., Chesta, C., Chin-Hui Lee. Joint maximum a posteriori adaptation of transformation and HMM parameters. Speech and Audio Processing, IEEE Transactions on, 2001, 9(4): 417-428.
[136] Baggenstoss, P.M. A modified Baum-Welch algorithm for hidden Markov models with multiple observation spaces. Speech and Audio Processing, IEEE Transactions on, 2001, 9(4): 411-416.
[137] Qiang Huo, Bin Ma. Online adaptive learning of continuous-density hidden Markov models based on multiple-stream prior evolution and posterior pooling. Speech and Audio Processing, IEEE Transactions on, 2001, 9(4): 388-398.
[138] Kan-Wing Mak, B., Bocchieri, E. Direct training of subspace distribution clustering hidden Markov model. Speech and Audio Processing, IEEE Transactions on, 2001, 9(4): 378-387.
[139] Mokbel, C. Online adaptation of HMMs to real-life conditions: a unified framework. Speech and Audio Processing, IEEE Transactions on, 2001,, 9(4): 342-357.
[140] Churchill G.A. Accurate restoration of DNA sequences. Case study in Bayes statistics, vol.2. Berlin: 1995.
[141] 邓明华，钱敏平．基于隐马氏模型(HMM)的手写体汉字识别及其实现．第三届中国计算机智能与智能应用会议学术论文集．北京：电子工业出版社，1997．
[142] Kee-Choon Kwona, Jin-Hyung Kim. Accident identification in nuclear power plants using hidden Markov models. Engineering Applications of Artificial Intelligence, 1999, 12:491-501.
[143] Y. Linde, A. Buzo, R.M. Grey. An algorithm for vector quantizer design. IEEE Trans. Communication, COM-28,1980, 1:84-95.
[144] Veprek, P., Bradley, A.B. An improved algorithm for vector quantizer design. IEEE Signal Processing Letters, 2000, 7(9): 250-252.
[145]申弢，黄树红，韩守木，刘德昌．基于SOFM网络的机械设备多类型信息融合与状态识别．机械工程学报．2001，37(1)：37-41．
[146] Lou R C, Kay M G. Multisensor integration and fusion in intelligence system. IEEE Trans. on Systems, Man and Cybernetics, 1989, 19(5): 901-931.
[147] Houselle S, Griaudou G. Contribution to multisensor fusion formalization. Robotics and Autonomous System. 1994, 13: 59-85.
[148] Lou R C, Kay M G. Multisensor integration and fusion: issue and approaches. Proc. of SPIE, Sensor Fusion. 1988, 42.
[149] 袁小宏，屈梁生．机械故障诊断中的信息融合利用问题研究．振动测试与诊断．1999，19(3)：187-192．
[150] Dasarathy B V. Sensor fusion potential exploitation-innovative architecture and illustrate application. Proceedings of the IEEE, 1997, 85(1): 29-36.
[151] 王耀南，李树涛．多传感器信息融合及其应用综述．控制与决策．2001，16(5)：518-522．
[152] 李桂英．一种基于多传感器融合技术的报警系统设计方法研究．长春光学精密机械学院学报．2001，024(002)：22-25．
[153] 朱名铨，蔡永霞．刀具磨损估计的多传感器融合神经网络方法研究．航空学报．1997，18(1)：120-122．
[154] 孙皓莹，蒋静坪．基于参数估计的多传感器数据融合．传感器技术．1995，(6)：32-36．
[155] 申弢，韩守木．分级自适应多传感器融合与机械振动监测诊断．华中理工大学学报．2000，28(8)：38-40．
[156] 王江萍．基于多传感器融合信息的故障诊断．机械科学与技术．2000，19(6)：950-952，943．
[157] 夏虹，曹欣荣．基于传感器融合的机械设备故障诊断的方法与系统．哈尔滨工程大学学报．1998，19(4)：52-57．
[158] 童进．隐Markov模型在旋转机械升降速过程故障诊断中的应用研究．[博士学位论文]，浙江大学，1999．
[159] 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．
[160] 杨而辅，徐用懋，张振鹏．一种应用机理模型和SOM神经网络液体火箭推进系统实时故障诊断方法．上海海运学院学报，2001，22(3)：281-284,390．
[161] A.B. Poritz. Linear predictive hidden Markov models and the speech signal. Proe. ICASSP 82(Paris, France), 1982, 3:1291-1294
[162] B.H. Juang, L.R. Rabiner. Mixture autoregressive hidden Markov models for speech signals. IEEE Trans. Acoustics, Speech, Signal Proe., 1985, ASSP-33(6): 1404-143.
[163] 屈梁生等．全息谱十年：回顾与展望．振动、测试与诊断，1998，4(18)：235-242．
[164] 黄载禄等．高阶神经网络与二阶隐马尔可夫模型的统一的数学模型．华中理工大学学报．1993，21(6)：1-5．
[165] 战普明等．语音识别隐马尔可夫模型的改进．电子学报．1994，22(1)：9-15．
[166] 黄文虎等．设备故障诊断技术原理、技术及应用．北京：科学技术出版社．1996．
[167] 楼向明等．旋转机械转子不平衡在线识别方法的试验研究．汽轮机技术．1998，4：222-226．
[168] 屈梁生．计算机辅助监测与诊断技术．西安：西安交通大学出版社，1989．
[169] 林建辉．基于神经网络的故障诊断．机械科学与技术．1998，3：437-439．
[170] 周川等．基于神经网络的非线性观测器及在线故障诊断．数据采集与处理．1998，3：206-209．
[171] 王江萍．机械故障信号主分量的最大熵谱分析．机械科学与技术．1998，6：980-982．
[172]何正嘉等．故障诊断案例选编．西安：西安交通大学出版社．1991．
[173]虞和济．设备故障诊断技术的现状及其发展．基础自动化．1996，5：1-5．
[174]牛明忠等．设备振动故障识别方法与实例．北京：冶金工业出版社．1995．
[175]李白诚等．信号智能分析技术与设备故障诊断．动态分析与测试技术．1994，3：16．
[176]蔡进．大型设备状态监测与故障诊断技术现状与发展．动态分析与测试技术．1996，3：26-31．
[177]钟秉林等．智能化故障诊断理论与方法的研究现状和展望．东南大学学报．1993，23(9)：1-11．
[178]张贤达．时间序列分析—高阶统计量方法．北京：清华大学出版社．1996．
[179]张贤达．现代信号处理．北京：清华大学出版社．1995．
[180]杨叔子等．时间序列分析的工程应用．武汉：华中理工大学出版社．1991．
[181]黄振华等．模式识别原理．杭州：浙江大学出版社．1991．
[182]黄德双．神经网络、模式识别系统理论．北京：电子工业出版社．1996．
[183]俞瑞钊等．人工智能原理与技术．杭州：浙江大学出版社．1993．
[184]李寻珍．旋转机械常见故障的试验研究．汽轮机技术．1998，1：33-38．
[185]高洪涛，王敏．证据理论在旋转机械综合故障诊断中应用．大连理工大学学报．2001，41(4)：459-462．
[186]王志鹏，马孝江．基于RBF网络的旋转机械故障诊断方法．大连理工大学学报．2001，41(6)：696-700．
[187]邹剑，陈进等．开闭裂纹转子的建模与弯曲刚度特性研究．振动与冲击．2001，20(4)：47-49，61．
[188]冯志鹏，谢宇等．旋转机械振动故障诊断理论与技术进展综述．振动与冲击．2001，20(4)：36-39．
[189]黄石红，陈勇等．时序信号处理在旋转机械故障诊断中的应用研究．汽轮机技术．2001，43(6)：355-356．
[190]胡劲松，严拱标等．一种可靠的旋转机械键相信号预处理电路．汽轮机技术．2001 43(6)：331-333．
[191]季厌庸，瞿红春等．基于虚拟仪器和神经网络的旋转机械故障诊断．中国民航学院学报．2002，20(1)：29-33．
[192]李志农，丁启全等．旋转机械典型故障的高阶谱特性分析．汽轮机技术．2002，44(1)：42-44．
[193]邓小文，Robert Liebich等．碰摩转子的弯曲和扭转振动分析．航空动力学报．2002，17(1)：97-104．
[194]何正嘉，訾艳阳．大型机械设备变工况非平稳动态分析与监测诊断关键技术．中国机械工程．1999，10(9)：978-981．
[195]徐光华，屈梁生．基于概率神经网络的大机组组合故障识别．机械科学与技术．2000，19(1)：85-87．
[196]徐光华，屈梁生．基于概率神经网络的机组状态多步预报方法．西安交通大学学报．1999，33(7)：
89-93．
[197]邹益民．MATLAB与C语言的混合编程．石油化工自动化．2000，(2)：51-53．
[198]陶红，袁金荣．MATLAB与C／Visual C++混合编程的实现．计算机工程与应用．2000，36(10)：100-101，104．
[199]牛小兵，牛子吉．MATLAB与VC++混合编程技巧．现代计算机：下半月刊．2001，(01B)：94-95．
[200]钱云，夏祖明．VC++和Matlab混合编程方法讨论．电脑编程技巧与维护．2001，(2)：61-62．
[201]谭志芳，刘一峰等．Matlab与C／C++语言混合编程的几种方法．微型机与应用．2001，20(2)：8-10．
[202]林知明，杨高波．基于Matlab和C++Builder的数字滤波仿真实现．电力机车技术．2001，24(1)：31-33．
[203]肖永韧，解习农等．VC与MATLAB混合编程之DLL实现方法．计算机工程与应用．2001，37(13)：174-176．
[204]范晖．VC与MATLAB的混合编程．现代电子．2001，(1)：21-25．
[205]钟舜聪，丁康等．MATLAB与VB混合编制信号分析与处理系统软件包．微型电脑应用．2001，17(6)：36-39．
[206]郭春华．MATLAB在转子振动固有频率计算中的应用．四川轻化工学院学报．2001，14(4)：55-60．
[207]胡建华，何清华．MATLAB与Visual C++／Delphi联合编程的研究．电脑与信息技术．2001，9(6)：39-41．
[208]高英杰，王海芳等．Matlab神经网络工具箱在系统辨识中的应用．液压气动与密封．2001，(5)：34-36．
[209]严建华，鞠鲁粤．基于VC++与MATLAB的高阶伺服系统参数分析．上海大学学报：自然科学版．2002，8(1)：51—54，67．
[210]张晋．采用MATLAB进行振动试验数据的处理．工业建筑．2002，32(2)：28-30，65．
[211]刘满华，任正云等．监控平台软件与MATLAB的DDE通信．微型电脑应用．2002，18(1)：27-29，26．
[212]刘志俭等编著．Matab应用程序接口用户指南．北京：科学出版社．2000．
[213]曾黄麟．粗糙集理论及其应用．重庆：重庆大学出版社．1996．
[2] 刘雄，赵振毅，屈梁生编著．转子监测和诊断系统．西安：西安交通大学出版社，1991．
[3] 吴今培，肖建华著．智能故障诊断与专家系统．北京：科学出版社，1997．
[4] 程耀东编著．机械振动学．杭州：浙江大学出版社，1988．
[5] 杨叔子等．时间序列分析的工程应用．武汉：华中理工大学出版社，1991．
[6] 张贤达．时间序列分析—高阶统计量方法．北京：清华大学出版社，1996．
[7] 雷继尧，丁康编著．轴承故障诊断．西安：西安交通大学出版社，1991．
[8] Sohre J S. Trouble-shooting to stop vibration of centrifugal. Petro/Chem engineering, 1968, 11: 22-33.
[9] 白木万博阳[日]．机械振动讲演论文集．郑州机械研究所．1984．
[10] 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．
[11] 汪慰军．分岔与混沌理论在大型旋转机械非线性故障诊断中的应用研究．[博士学位论文]，浙江大学，杭州．1998．
[12] 曾复．裂纹转子非线性振动机理及实验研究．[博士学位论文]．浙江大学，杭州，2000．
[13] 黄文虎等．机械故障的数学方法．第三届全国振动理论及应用学术会议论文．1987．
[14] 夏松波 张礼勇．旋转机械故障诊断技术的现状与展望．振动与冲击．1997，16(2)：1-5．
[15] 冯志鹏，谢宇等．旋转机械振动故障诊断理论与技术进展综述．振动与冲击．2001，20(4)：36-39．
[16] J.D. Stuart, et al. Turbo Mac-An expert system to aid in the diagnosis of cause of vibration producing problems in large turbo machinery. Proc. of ASME Computer in Eng. Cant Boston in USA. 1985, 217-319.
[17] M. F. White, et al. Expert system based diagnosis of turbo machinery. Proc. of IMEKO, 5th Int Symptom Technical Diagnosis, WG. 1987: 421-429.
[18] 徐敏．欧美大型旋转机械监测诊断技术发展近况与评述．第四届全国机械设备故嚎诊断学术会议论文集．杭州：1994：3-9．
[19] 徐世吕，王日新，刘占生等．“八五”国家重点科技项目验收报告，哈尔滨电工仪表研究所，1996(3)．
[20] 刘荣强，夏松波等．汽轮机发电机组轴系稳定性的诊断方法，汽轮机技术，1994(12)．
[21] 杨叔子等．机械设备的诊断理论、技术与方法．振动工程学报．1992，5：193—201．
[22] 钟一锷等编．转子动力学．北京：清华大学出版社，1987．
[23] 吴今培编著．模糊诊断理论及其应用．北京：科学出版社，1995．
[24] 沈清、胡德文、时春编著．神经网络应用技术．长沙：国防科技大学出版社，1993．
[25] 张孝令，刘福升等编著．贝叶斯动态模型及其预测．济南：山东科学技术出版社，1992．
[26] A General Architecture for Supervised Classification of Multivariate Time Series. http://www.cse.unsw.edu.au/～waleed/phd/tr9806/tr.html.
[27] Alexander Ypma, Co Melissant, Ole Baunbaek-Jensen, Robert P. W. Duin. Health Monitoring with Learning Methods. ICANN 2001: 547-553.
[28] David M. J. Tax, Alexander Ypma, Robert P. W. Duin. Pump Failure Detection Using Support Vector Data Descriptions. IDA 1999:415-426.
[29] 谢锦辉．隐Markov模型(HMM)及其在语音处理中的应用．武汉：华中理工大学出版社，1995．
[30] Geman S, Geman D. Stochastic Relaxation, Gibbs Distribution, and the Bayesian Restoration of Images. IEEE Trans. PAMI, 1984, 6(5).
[31] 钱敏平，龚光鲁．从数学角度看计算智能．科学通报，1998，43(16)：1681-1792．
[32] Li，CJ，邱晓明．模式识别分析在线检测轴承局部缺陷．国外轴承．1990，(6)：41-47，39．
[33] 余国安，彭勇．应用模式识别技术实现抽油机井下故障的自动诊断．西安石油学院学报．1990，5(1)：23-33．
[34] Aitkin M. WiLson G. Mixture models, outline and the EM algorithm. Techno metrics, 1980,22(3):325～331
[35] Qing Wang, Zheru Chi, Feng, D.D., Rongchum Zhao. Hidden Markov random field based approach for off-line handwritten Chinese character recognition. Pattern Recognition, 2000. Proceedings. 15th International Conference on, 2000, 2:347-350.
[36] 陈锡先，王立健．多讲话者汉语单字识别．北京邮电学院学报．1989，12(2)：1-7．
[37] 徐文盛，戴蓓倩等．一种抗噪孤立字语音识别模型．中国科学技术大学学报．2000，30(6)：659-665．
[38] 方绍武，戴蓓倩．一种离散隐Markov模型参数的全局优化算法．电路与系统学报．2000，5(3)：78-81．
[39] 童进，吴昭同等．数字离散信号滤器HMM综合平均法的研究．浙江大学学报，工学版．2000，34(3)：-312-316．
[40] 郭庆，柴海新．隐Markov模型中状态停留时间的模型化．清华大学学报：自科版．1999，39(5)：97-100．
[41] 童进，吴昭同等．大型旋转机械升降速过程故障诊断研究．振动测试与诊断．1999，19(3)：193-195．
[42] 童进 吴昭同等．大型旋转机械升降速过程故障诊断HMM方法研究．机械科学与技术(西安)．1999，18(6)：973-975．
[43] 童进 吴昭同等．大型旋转机械升降速过程故障诊断HMM-AR方法研究．振动与冲击．1999，18(2)：79-80．
[44] 周德全，刘国岁．基于隐Markov模型的雷达目标识别技术研究．南京理工大学学报．1998，22(3)：224-227，251．
[45] 程俊，易克初．一种基于小波变换和隐Markov模型的声调识别方法．电子科学学刊．1997．19(2)：177-182．
[46] 高雨清，陈永彬．隐Markov模型参数估计的一种新方法．自动化学报．1991，17(1)：56-62．
[47] 方高林，高文等．基于SOFM／HMM模型的非特定人手语识别系统．计算机学报．2002，25(1)：16-21．
[48] 姚鸿勋，高文等．基于色度分析的唇动特性提取与识别．电子学报．2002，30(2)：168-172．
[49] 茅晓泉，胡光锐等．一种DHMM的混合训练方法．电子学报．2002，30(1)：148-150．
[50] 谢贤亚，李焕润等．EM—HMM多频率线跟踪算法．电子学报．2002，30(1)：22-26
[51] 刘鸣，戴蓓清等．鲁棒性话者辨识中的一种改进的马尔科夫模型．电子学报．2002，30(1)：46-48．
[52] 方绍武，戴蓓倩等．一种具有强分类能力的离散HMM训练算法．软件学报．2001，012(010)：1540-1543
[53] 李灿伟，杨震．基于HMM的说话人确认系统的研究．南京邮电学院学报：自然科学版．2001，021(002)：52-55，70
[54] 茅晓泉，胡光锐．基于最大互信息的离散隐马尔柯夫模型训练方法．上海交通大学学报．2001，035(011)：1713—1716．
[55] 赵力，邹采荣等．基于PCANN／HMM混合结构的语音识别方法．信号处理．2001，017(005)：473-476
[56] 冯长建，丁启全等．SOFM和HMM在旋转机械升降速全过程故障诊断中的应用．上海海运学院学报．2001，022(003)：98-100，104．
[57] 李士进，杨静宇等．基于奇异值特征和隐马尔可夫模型的人脸检测．中国图像图形学报．2001，6(7)：681—688．
[58] 陈友仁，赵正校．基于隐马尔可夫模型的车牌自动识别技术．红外与激光工程．2001，30(2)：102-107．
[59] 李姣军．小波变换和HMM模型在语音识别中的应用．重庆大学学报：自然科学版．2001，24(4)：61-63
[60] 赵力，刘怡龙等．基于VQ—HMM的无教师说话人自适应方法．东南大学学报．2001，31(2)：23-26
[61] 王春立，高文．具有不同数目状态结点的HMMs在中国手语识别中的应用．计算机研究与发展．2001．38(1)：-111-115．
[62] 王春启，汪鸿振．基于HMM的瞬态回波分类研究．声学技术．2000，19(1)：6-8．
[63] 何强，张有为．多观察序列连续隐含马尔柯夫模型的无溢出参数重估．电子学报．2000，28(10)：98-101．
[64] 秦姣华，向旭宇．HMM在汉字识别技术中的应用．现代计算机：下半月刊．2000，(97)：29-31．
[65] 邓伟，赵劳椿．隐马尔可夫模型的一种有区分力的反向传播训练方法．自动化学报．2000，26(4)：492-498．
[66] 姚丹霖，陈火旺．多链ANN-HMM识别模型及其应用．模式识别与人工智能．2000，13(1)：1—6．
[67] 张杰，黄志同．语音识别中隐马尔可夫模型状态数的选取原则及研究．计算机工程与应用．2000，36(1)：67-69，133．
[68] 马晓旭，张传利．基于离散隐马尔科夫模型的变压器励磁涌流鉴别新算法．电力系统自动化．2000，24(3)：23-28．
[69] 贺前华，韦岗．基于遗传算法的HMM最小错误率训练方法．电路与系统学报．1999，4(4)：46-50
[70] Heck, L.P. Signal processing research in automatic tool wear monitoring. Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on, 1993, 1:55-58.
[71] Heck, L.P., McClellan, J.H. Mechanical system monitoring using hidden Markov models, coustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on, 1991, 3:1697-1700.
[72] Smyth, P. Hidden Markov models and neural networks for fault detection in dynamic systems. Neural Networks for Processing [1993] Ⅲ. Proceedings of the 1993 IEEE-SP Workshop, 1993:582-592.
[73] Ertunc, H.M., Loparo, K.A., Ozdemir, E., Ocak, H. Real time monitoring of tool wear using multiple modeling method. Electric Machines and Drives Conference, 2000. IEMDC 2001. IEEE International, 2001:687-691.
[74] Hatzipantelis, E., Penman, J. The use of hidden Markov models for condition monitoring electrical machines. Electrical Machines and Drives, 1993. Sixth International Conference on, 1993:91-96.
[75] Ocak, H., Loparo, K.A. new bearing fault detection and diagnosis scheme based on hidden Markov modeling of vibration signals. Acoustics, Speech, and Signal Processing, 2001. Proceedings. 2001 IEEE International Conference on, 2001, 5:3141-3144.
[76] Atlas, L., Ostendorf, M., Bernard, G.D. Hidden Markov models for monitoring machining tool-wear. Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on, 2000, 6:3887-3890.
[77] Owsley, L.M.D., Atlas, L.E.; Bernard, G.D. Automatic clustering of vector time-series for
manufacturing machine monitoring. Acoustics, Speech, and Signal Processing, 1997． ICASSP-97． , 1997 IEEE International Conference on , 1997, 4: 3393-3396．
[78] Youmin Zhang, Li, X.R., Kemin Zhou. A fault detection and diagnosis approach based on hidden Markov chain model. American Control Conference, 1998． Proceedings of the 1998, 4: 2012-2016．
[79] Loparo, K.A., Adams, M.L., Lin, W., Abdel-Magied, M.F., Afshari, N. Fault detection and diagnosis of rotating machinery. Industrial Electronics, IEEE Transactions on , 2000,47(5) : 1005-1014．
[80] Wattar, I., Hafez, W., Gao, Z. Model-based diagnosis of chaotic vibration signals. Industrial Electronics Society, 1999． IECON '99 Proceedings. The 25th Annual Conference of the IEEE, 1999, 3:1177-1180．
[81] Stegemann, D., Reimche, W., Sudmersen, U., Pietsch, O., Liu, Y. Monitoring and vibrational diagnostic of rotating machinery in power plants. Power Station Maintenance-Profitability Through Reliability, 1998． First IBE/IMechE International Conference on (Conf. Publ. No. 452) , 1998: 39-44．
[82] Abdel-Magied, M.F., Loparo, K.A., Wei Lin. Fault detection and diagnosis for rotating machinery: a model-based approach. American Control Conference, 1998． Proceedings of the 1998, 6: 3291-3296．
[83] McCormick, A.C., Nandi, A.K. A comparison of artificial neural networks and other statistical methods for rotating machine condition classification. Modeling and Signal Processing for Fault Diagnosis (Digest No.: 1996/260) , IEE Colloquium on , 1996, 2: 1-6．
[84] Tanaka, M., Sakawa, M., Shiromaru, I., Matsumoto, T. Application of Kohonen's self-organizing network to the diagnosis system for rotating machinery. Systems, Man and Cybernetics, 1995． Intelligent Systems for the 21st Century., IEEE International Conference on, 1995, 5: 4039-4044．
[85] Barker, R.W., Hinich, M.J. Statistical monitoring of rotating machinery by cumulate spectral analysis. Higher-Order Statistics, 1993． , IEEE Signal Processing Workshop on, 1993: 187-191．
[86] Gaunard, P., Mubikangiey, C.G, Couvreur, C., Fontaine, V. Automatic classification of environmental noise events by hidden Markov models. Acoustics, Speech and Signal Processing, 1998． Proceedings of the 1998 IEEE International Conference on, 1998, 6: 3609-3612．
[87] Winger, L.L. Linearly constrained generalized Lloyd algorithm for reduced codebook vector quantization. Signal Processing, IEEE Transactions on, 2001, 49(7) : 1501-1509．
[88] Gader, P.D., Mystkowski, M., Yunxin Zhao. Landmine detection with ground penetrating radar using hidden Markov models. Geosciences and Remote Sensing, IEEE Transactions on, 2001, 39(6) : 1231-1244．
[89] Hasegawa, T., Yasuda, H., Matsumoto, T. Fast discrete HMM algorithm for online handwriting
recognition. Pattern Recognition, 2000． Proceedings. 15th International Conference on, 2000,4: 535-538．
[90] Jaehak Chung, Powers, E.J., Grady, W.M., Bhatt, S.C. Electric power transient disturbance classification using wavelet-based hidden Markov models. Acoustics, Speech, and Signal Processing, 2000． ICASSP '00． Proceedings. 2000 IEEE International Conference on, 2000,6: 3662-3665．
[91] Qiang Hue; Smith, N.; Bin Ma. Efficient ML training of CDHMM parameters based on prior evolution, posterior intervention and feedback. Acoustics, Speech, and Signal Processing, 2000． ICASSP '00． Proceedings. 2000 IEEE International Conference on, 2000, 2: 1001-1004．
[92] Runkle, P., Carin, L., Lam Nguyen. Multi-aspect target classification using hidden Markov models for data fusion. Geosciences and Remote Sensing Symposium, 2000． Proceedings. IGARSS 2000, 5: 2123-2125．
[93] Zhao, Y. Spectrum estimation of HMM signal source in channel distortion and additive noise. Signal Processing Proceedings, 2000． WCCC-ICSP 2000． 5th International Conference on, 2000, 1: 226-229．
[94] Chen P, Kobayashi, H. Semi-blind block channel estimation and signal detection using hidden Markov models. Global Telecommunications Conference, 2000． GLOBECOM '00． IEEE, 2000, 2: 1051-1055．
[95] Azzouzi, M., Nabney, I.T. Modeling financial time series with switching state space models. Computational Intelligence for Financial Engineering, 1999． (CIFEr) Proceedings of the IEEE/IAFE 1999 Conference on, 1999: 240-249．
[96] Liehr, S., Pawelzik, K., Kohlmorgen, J., Lemm, S., Muller, K.R. Hidden Markov mixtures of experts for prediction of non-stationary dynamics. Neural Networks for Signal Processing Ⅸ, 1999． Proceedings of the 1999 IEEE Signal Processing Society Workshop, 1999: 195-204．
[97] Matrouf, D., Gauvain, J.L. Using AR HMM state-dependent filtering for speech enhancement. Acoustics, Speech, and Signal Processing, 1999． Proceedings. 1999 IEEE International Conference on, 1999,2:785-788．
[98] Fang Sun, Guangrui Hu. Speech recognition based on genetic algorithm for training HMM. Electronics Letters, 1998,34(16) : 1563-1564．
[99] L.R. Rabiner. A tutorial on Hidden Markov Models and selected application in speech recognition. Proc. IEEE, 1989, 77(2) : 257-286．
[100] Bahl L R, Brown P F, De Souza P V, et al. A New Algorithm for the Estimation of Hidden Markov Models Parameters. Proc. ICASSP'88,1988: 493-496．
[101] Bahl L R, Jelinek F. Decoding for Channels with Insertions, Deletions, and Substitution with
Applications to Speech Recognition .IEEE Trans. IT. 1975,21(2) : 401-411．
[102] Baker J K. The DRAGON System-An Overview. IEEE Trans. ASSP. 1975,23(1) : 24-29．
[103] Nadas A, et al. On a Model-Robust Training Method for Speech Recognition. IEEE Trains. ASSP, 1988,36(9) : 1432-1436．
[104] Rabiner L R, Juang B H. An Introduction to Hidden Markov Models. IEEE ASSP Magazine. 1986, 3(1) : 4-6．
[105] Rabiner L R, et al. On the Use of Hidden Markov Models for Speaker Independent Recognition of Isolated Word from a Medium-Size Vocabulary. AT&T Bell Tab. Tech. J. 1984, 63(4) : 627-642．
[106] Bahl L R, Brown P F, et al. Maximum Mutual Information Estimation of Hidden Markov Parameters for Speech Recognition. Proc. ICASSP'86,1986: 49-52．
[107] Normandin Y, Morgan S D. An Improved MMIE Training Algorithm for Speaker-Independent, Small Vocabulary, Continuous Speech Recognition. Proc. ICASSP'91,1991: 537-540．
[108] Juang B H, Rabiner L R. The Segmental k-Means Algorithm for Estimating Parameters of Hidden Markov Models. IEEE Trans. ASSP, 1990, 38(9) : 1639-1641．
[109] Young S J. Competitive Training in Hidden Markov Models. Proc. ICASSP'88,1988: 28-31．
[110] J Pardey, S Roberts, et al. A Review of Parametric Modeling Techniques for EEG Analysis. Med. Eng. Phys., 1996,18(1) : 2-11．
[111] D W Skagen. Estimation of running frequency spectra using a Kalman filtering algorithm. Journal of Biomedical Engineering, 1988,10: 275-279．
[112] R J Meinhold, N D Singpurwalla. Understanding the Kalman Filter. The American Statistician, 1983, 37(2) : 123-127．
[113] A H Jazwinski. Adaptive filtering. Automation, 1969, 5:475-485．
[114] C M Bishop. Neural networks of pattern recognition. Oxford University Press, Oxford, 1995．
[115] Ephraim Y, Dembo A, Rabiner L R. A Minimum Discrimination Information Approach for Hidden Markov Modeling. Proc. ICASSP'87, 1987: 25-28．
[116] Ephraim Y, Rabiner L R. On the Relation between Modeling Approaches for Information Source. Proc. ICASSP'88, 1988: 24-27．
[117] Merialdo B. Phonetic Recognition Using Hidden Markov Models and Maximum Mutual Information Training. Proc. ICASSP'88,1988: 111-114．
[118] White, L.B. A comparison between optimal and filtering for hidden Markov processes. IEEE Signal Processing Letters, 1998, 5(5) : 124-126．
[119] http://www.ee.ic.ac.uk/hp/stafT/dmb/voicebox/voicebox.html.
[120] Cheng, W.T., Chan, K.L. Classification of electrocardiogram using hidden Markov models.
Engineering in Medicine and Biology Society, 1998． Proceedings of the 20th Annual International Conference of the IEEE, 1998,1: 143-146．
[121] Sahinci, E., Kamen, E.W. Detection of discrete faults in electronics assembly. Control Applications, 1998． Proceedings of the 1998 IEEE International Conference on, 1998, 2: 776-780．
[122] Azzouzi, M., Nabney, I.T. Analyzing time series structure with hidden Markov models. Neural Networks for Signal Processing Ⅷ, 1998． Proceedings of the 1998 IEEE Signal Processing Society Workshop, 1998:402-408．
[123] Logan, B., Moreno, P. Factorial HMMs for acoustic modeling. Acoustics, Speech and Signal Processing, 1998． Proceedings of the 1998 IEEE International Conference on, 1998,2: 813-816．
[124] Owsley, L.M.D., Atlas, L.E., Bernard, GD. Self-organizing feature maps and hidden Markov models for machine-tool monitoring. Signal Processing, IEEE Transactions on, 1997,45(11) : 2787-2798．
[125] Kwong, S., Chau, C.W. Analysis of parallel genetic algorithms on HMM based speech recognition system. Consumer Electronics, IEEE Transactions on, 1997,43(4) : 1229-1233．
[126] Kundu, A., Chen, QC. An integrated hybrid neural network and hidden Markov model classifier for sonar signals. Signal Processing, IEEE Transactions on, 1997,45(10) : 2566-2570．
[127] Stein, D.W.J., Dillard, GM. Applying hidden Markov models to radar detection in clutter. Radar 97 (Conf. Publ. No. 449) , 1997: 586-590．
[128] Chen, B., Willett, P. Quickest detection of hidden Markov models. Decision and Control, 1997, Proceedings of the 36th IEEE Conference on, 1997,4: 3984-3989．
[129] Kottke, D.P., Jong-Kae Fwu; Brown, K. Hidden Markov modeling for automatic target recognition. Signals, Systems & Computers, 1997． Conference Record of the Thirty-First Asilomar Conference on, 1997,1:859-863．
[130] Ramachandrula, S., Thippur, S. Scalar quantization of features in discrete hidden Markov models. Information, Communications and Signal Processing, 1997． ICICS. Proceedings of 1997 International Conference on, 1997,1: 537-540．
[131] Owsley, L.M.D., Atlas, L.E., Bernard, GD. Automatic clustering of vector time-series for manufacturing machine monitoring. Acoustics, Speech, and Signal Processing, 1997． ICASSP-97． , 1997 IEEE International Conference on, 1997,4: 3393-3396．
[132] Bengio, Y., Frasconi, P. Input-output HMMs for sequence processing. Neural Networks, IEEE Transactions on, 1996, 7(5) : 1231-1249．
[133] Ruey S. Huang, Chung J. Kuo, Ling-Ling Tsai, Chen, O.T.C. EEG pattern recognition-arousal states detection and classification. Neural Networks, 1996． IEEE International Conference on, 1996, 2: 641-646．
[134] Guoliang Fan, Xiang-Gen Xia. mage denoising using a local contextual hidden Markov model in the wavelet domain. IEEE Signal Processing Letters, 2001, 8(5): 125-128.
[135] Siohan, O., Chesta, C., Chin-Hui Lee. Joint maximum a posteriori adaptation of transformation and HMM parameters. Speech and Audio Processing, IEEE Transactions on, 2001, 9(4): 417-428.
[136] Baggenstoss, P.M. A modified Baum-Welch algorithm for hidden Markov models with multiple observation spaces. Speech and Audio Processing, IEEE Transactions on, 2001, 9(4): 411-416.
[137] Qiang Huo, Bin Ma. Online adaptive learning of continuous-density hidden Markov models based on multiple-stream prior evolution and posterior pooling. Speech and Audio Processing, IEEE Transactions on, 2001, 9(4): 388-398.
[138] Kan-Wing Mak, B., Bocchieri, E. Direct training of subspace distribution clustering hidden Markov model. Speech and Audio Processing, IEEE Transactions on, 2001, 9(4): 378-387.
[139] Mokbel, C. Online adaptation of HMMs to real-life conditions: a unified framework. Speech and Audio Processing, IEEE Transactions on, 2001,, 9(4): 342-357.
[140] Churchill G.A. Accurate restoration of DNA sequences. Case study in Bayes statistics, vol.2. Berlin: 1995.
[141] 邓明华，钱敏平．基于隐马氏模型(HMM)的手写体汉字识别及其实现．第三届中国计算机智能与智能应用会议学术论文集．北京：电子工业出版社，1997．
[142] Kee-Choon Kwona, Jin-Hyung Kim. Accident identification in nuclear power plants using hidden Markov models. Engineering Applications of Artificial Intelligence, 1999, 12:491-501.
[143] Y. Linde, A. Buzo, R.M. Grey. An algorithm for vector quantizer design. IEEE Trans. Communication, COM-28,1980, 1:84-95.
[144] Veprek, P., Bradley, A.B. An improved algorithm for vector quantizer design. IEEE Signal Processing Letters, 2000, 7(9): 250-252.
[145]申弢，黄树红，韩守木，刘德昌．基于SOFM网络的机械设备多类型信息融合与状态识别．机械工程学报．2001，37(1)：37-41．
[146] Lou R C, Kay M G. Multisensor integration and fusion in intelligence system. IEEE Trans. on Systems, Man and Cybernetics, 1989, 19(5): 901-931.
[147] Houselle S, Griaudou G. Contribution to multisensor fusion formalization. Robotics and Autonomous System. 1994, 13: 59-85.
[148] Lou R C, Kay M G. Multisensor integration and fusion: issue and approaches. Proc. of SPIE, Sensor Fusion. 1988, 42.
[149] 袁小宏，屈梁生．机械故障诊断中的信息融合利用问题研究．振动测试与诊断．1999，19(3)：187-192．
[150] Dasarathy B V. Sensor fusion potential exploitation-innovative architecture and illustrate application. Proceedings of the IEEE, 1997, 85(1): 29-36.
[151] 王耀南，李树涛．多传感器信息融合及其应用综述．控制与决策．2001，16(5)：518-522．
[152] 李桂英．一种基于多传感器融合技术的报警系统设计方法研究．长春光学精密机械学院学报．2001，024(002)：22-25．
[153] 朱名铨，蔡永霞．刀具磨损估计的多传感器融合神经网络方法研究．航空学报．1997，18(1)：120-122．
[154] 孙皓莹，蒋静坪．基于参数估计的多传感器数据融合．传感器技术．1995，(6)：32-36．
[155] 申弢，韩守木．分级自适应多传感器融合与机械振动监测诊断．华中理工大学学报．2000，28(8)：38-40．
[156] 王江萍．基于多传感器融合信息的故障诊断．机械科学与技术．2000，19(6)：950-952，943．
[157] 夏虹，曹欣荣．基于传感器融合的机械设备故障诊断的方法与系统．哈尔滨工程大学学报．1998，19(4)：52-57．
[158] 童进．隐Markov模型在旋转机械升降速过程故障诊断中的应用研究．[博士学位论文]，浙江大学，1999．
[159] 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．
[160] 杨而辅，徐用懋，张振鹏．一种应用机理模型和SOM神经网络液体火箭推进系统实时故障诊断方法．上海海运学院学报，2001，22(3)：281-284,390．
[161] A.B. Poritz. Linear predictive hidden Markov models and the speech signal. Proe. ICASSP 82(Paris, France), 1982, 3:1291-1294
[162] B.H. Juang, L.R. Rabiner. Mixture autoregressive hidden Markov models for speech signals. IEEE Trans. Acoustics, Speech, Signal Proe., 1985, ASSP-33(6): 1404-143.
[163] 屈梁生等．全息谱十年：回顾与展望．振动、测试与诊断，1998，4(18)：235-242．
[164] 黄载禄等．高阶神经网络与二阶隐马尔可夫模型的统一的数学模型．华中理工大学学报．1993，21(6)：1-5．
[165] 战普明等．语音识别隐马尔可夫模型的改进．电子学报．1994，22(1)：9-15．
[166] 黄文虎等．设备故障诊断技术原理、技术及应用．北京：科学技术出版社．1996．
[167] 楼向明等．旋转机械转子不平衡在线识别方法的试验研究．汽轮机技术．1998，4：222-226．
[168] 屈梁生．计算机辅助监测与诊断技术．西安：西安交通大学出版社，1989．
[169] 林建辉．基于神经网络的故障诊断．机械科学与技术．1998，3：437-439．
[170] 周川等．基于神经网络的非线性观测器及在线故障诊断．数据采集与处理．1998，3：206-209．
[171] 王江萍．机械故障信号主分量的最大熵谱分析．机械科学与技术．1998，6：980-982．
[172]何正嘉等．故障诊断案例选编．西安：西安交通大学出版社．1991．
[173]虞和济．设备故障诊断技术的现状及其发展．基础自动化．1996，5：1-5．
[174]牛明忠等．设备振动故障识别方法与实例．北京：冶金工业出版社．1995．
[175]李白诚等．信号智能分析技术与设备故障诊断．动态分析与测试技术．1994，3：16．
[176]蔡进．大型设备状态监测与故障诊断技术现状与发展．动态分析与测试技术．1996，3：26-31．
[177]钟秉林等．智能化故障诊断理论与方法的研究现状和展望．东南大学学报．1993，23(9)：1-11．
[178]张贤达．时间序列分析—高阶统计量方法．北京：清华大学出版社．1996．
[179]张贤达．现代信号处理．北京：清华大学出版社．1995．
[180]杨叔子等．时间序列分析的工程应用．武汉：华中理工大学出版社．1991．
[181]黄振华等．模式识别原理．杭州：浙江大学出版社．1991．
[182]黄德双．神经网络、模式识别系统理论．北京：电子工业出版社．1996．
[183]俞瑞钊等．人工智能原理与技术．杭州：浙江大学出版社．1993．
[184]李寻珍．旋转机械常见故障的试验研究．汽轮机技术．1998，1：33-38．
[185]高洪涛，王敏．证据理论在旋转机械综合故障诊断中应用．大连理工大学学报．2001，41(4)：459-462．
[186]王志鹏，马孝江．基于RBF网络的旋转机械故障诊断方法．大连理工大学学报．2001，41(6)：696-700．
[187]邹剑，陈进等．开闭裂纹转子的建模与弯曲刚度特性研究．振动与冲击．2001，20(4)：47-49，61．
[188]冯志鹏，谢宇等．旋转机械振动故障诊断理论与技术进展综述．振动与冲击．2001，20(4)：36-39．
[189]黄石红，陈勇等．时序信号处理在旋转机械故障诊断中的应用研究．汽轮机技术．2001，43(6)：355-356．
[190]胡劲松，严拱标等．一种可靠的旋转机械键相信号预处理电路．汽轮机技术．2001 43(6)：331-333．
[191]季厌庸，瞿红春等．基于虚拟仪器和神经网络的旋转机械故障诊断．中国民航学院学报．2002，20(1)：29-33．
[192]李志农，丁启全等．旋转机械典型故障的高阶谱特性分析．汽轮机技术．2002，44(1)：42-44．
[193]邓小文，Robert Liebich等．碰摩转子的弯曲和扭转振动分析．航空动力学报．2002，17(1)：97-104．
[194]何正嘉，訾艳阳．大型机械设备变工况非平稳动态分析与监测诊断关键技术．中国机械工程．1999，10(9)：978-981．
[195]徐光华，屈梁生．基于概率神经网络的大机组组合故障识别．机械科学与技术．2000，19(1)：85-87．
[196]徐光华，屈梁生．基于概率神经网络的机组状态多步预报方法．西安交通大学学报．1999，33(7)：
89-93．
[197]邹益民．MATLAB与C语言的混合编程．石油化工自动化．2000，(2)：51-53．
[198]陶红，袁金荣．MATLAB与C／Visual C++混合编程的实现．计算机工程与应用．2000，36(10)：100-101，104．
[199]牛小兵，牛子吉．MATLAB与VC++混合编程技巧．现代计算机：下半月刊．2001，(01B)：94-95．
[200]钱云，夏祖明．VC++和Matlab混合编程方法讨论．电脑编程技巧与维护．2001，(2)：61-62．
[201]谭志芳，刘一峰等．Matlab与C／C++语言混合编程的几种方法．微型机与应用．2001，20(2)：8-10．
[202]林知明，杨高波．基于Matlab和C++Builder的数字滤波仿真实现．电力机车技术．2001，24(1)：31-33．
[203]肖永韧，解习农等．VC与MATLAB混合编程之DLL实现方法．计算机工程与应用．2001，37(13)：174-176．
[204]范晖．VC与MATLAB的混合编程．现代电子．2001，(1)：21-25．
[205]钟舜聪，丁康等．MATLAB与VB混合编制信号分析与处理系统软件包．微型电脑应用．2001，17(6)：36-39．
[206]郭春华．MATLAB在转子振动固有频率计算中的应用．四川轻化工学院学报．2001，14(4)：55-60．
[207]胡建华，何清华．MATLAB与Visual C++／Delphi联合编程的研究．电脑与信息技术．2001，9(6)：39-41．
[208]高英杰，王海芳等．Matlab神经网络工具箱在系统辨识中的应用．液压气动与密封．2001，(5)：34-36．
[209]严建华，鞠鲁粤．基于VC++与MATLAB的高阶伺服系统参数分析．上海大学学报：自然科学版．2002，8(1)：51—54，67．
[210]张晋．采用MATLAB进行振动试验数据的处理．工业建筑．2002，32(2)：28-30，65．
[211]刘满华，任正云等．监控平台软件与MATLAB的DDE通信．微型电脑应用．2002，18(1)：27-29，26．
[212]刘志俭等编著．Matab应用程序接口用户指南．北京：科学出版社．2000．
[213]曾黄麟．粗糙集理论及其应用．重庆：重庆大学出版社．1996．