基于核模式分析方法的旋转机械性能退化评估技术研究
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摘要
随着科学技术的进步和工业需求的发展,各类先进生产设备一方面不断向复杂、高速、高效、轻型、微型或大型的方向发展,另一方面却又面临更加苛刻的工作和运行环境。一旦设备的关键部件发生故障,就可能破坏整台设备甚至影响整个生产过程,造成巨大经济损失,还可能导致灾难性的人员伤亡并产生严重的社会影响。因此,如何有效评估设备的运行状态,从而能够及时采取措施以防止灾难性事故的发生是当前迫切需要解决的问题。
设备从性能开始恶化到完全失效,要经过一个性能逐步退化的过程。如果能够在设备性能退化的过程中检测到设备性能退化的程度,那么就可以有针对地组织生产和设备维修,防止设备异常失效的发生。设备性能退化评估与预测正是基于以上思想提出的一种主动设备维护的技术。设备性能退化评估与预测侧重于对设备性能衰退状态全过程的走向预测,而不在于某个时间点的性能状态诊断,因此,其与现有的故障诊断技术在理念上和方法上都有很大的不同。本文以旋转机械的典型零部件为对象,深入开展了设备性能退化智能评估和预测的理论体系和技术方法研究,包括以下几个方面的内容:
从理论分析与工程应用的角度出发,阐述了论文的选题背景和研究意义。分析了设备性能退化评估方法、预测方法以及核模式分析方法等方面的国内外发展现状,总结了目前研究中需要解决的问题,确立了本论文的研究内容。
简要地叙述了核模式分析方法的原理,将小波核函数引入核模式分析方法中,研究了小波核函数在支持向量机分类器、支持向量机回归分析以及核主元分析中的应用。基于平移不变核函数Mercer条件,推导证明了Mexican hat小波函数构造的容许小波核函数。利用仿真数据和轴承试验数据,对比了小波核模式分析方法与RBF核模式分析方法的泛化能力,分析结果表明前者具有更好的性能。
提出了循环平稳熵分析方法,通过对轴承振动信号的相关性进行定量分析,准确地监测轴承运行状态。此外利用小波核函数核主元分析方法对多维特征向量进行约简,在全面掌握设备性能的同时,提高分析的效率并增强评估的准确性。利用轴承的试验数据对特征提取和特征约简方法进行了验证。
研究了基于支持向量机的多分类器性能退化评估方法。本文应用支持向量机二叉树算法进行设备性能退化评估研究,避免了传统支持向量机多分类器的拒识区域问题。将小波核函数引入支持向量机多分类器中,提高了分类器的分类精度。提出了几何距离概率统计准则,在保证分类精度的同时,提高支持向量机多分类器参数优选的效率。使用不同故障程度的轴承试验数据对核参数优化准则及分类算法进行了验证。
研究了基于支持向量机几何距离的性能退化评估模型。支持向量机算法在处理二分类问题时,首先利用核函数将特征向量映射到高维特征空间,然后在该特种空间中建立线性分类面进行分类。本文提出的评估方法利用设备状态特征向量与支持向量机最优分类面之间的几何距离,描述设备偏离正常状态的程度,从而实现对设备性能退化程度的定量分析。利用裂纹转子仿真模型,对评估模型的泛化推广能力进行了分析。根据拉依达准则,研究了连续变量的自适应报警阈值设定问题。裂纹转子和轴承疲劳试验数据验证了该评估方法的评估效果。
研究了基于小波核支持向量机自回归(WSVAR)模型的性能退化预测方法。研究了模型各个参数对预测结果的影响,并以二进制网格搜索方法选定模型最优参数。利用轴承加速疲劳试验全过程数据,对于WSVAR模型的预测效果进行了验证。并将WSVAR的预测结果分别与RBF核函数SVAR和RBF神经网络的预测结果进行了对比,表明WSVAR预测模型具有更高的预测精度。
进行了轴承强化疲劳实验研究。在轴承加速疲劳试验台上采集了多组轴承强化疲劳试验全过程数据,利用大量的滚动轴承信号验证了文中提出的各种特征提取方法、特征约简方法、性能退化评估和预测方法的适用性。
针对机械设备功能层次分级且相对独立的特点,提出了基于层次分析的设备整体性能退化评估系统模型。考虑系统计算量大及系统后续扩展性等问题,在系统的开发中引入了面向服务(SOA)技术,并基于SOA的成熟实现技术WCF开发了设备性能退化评估与预测系统原型。在某风机监测系统的现场应用验证了系统设计的有效性。
The rapid growth of technologies and market competition has already had a significant impact on commercial manufacture. The equipments development direction has some new characteristics, such as hugeness, distribution, high speed, automation and complexity. And also the equipments must face more and more harsh running condition. Once there is something wrong with them, production efficiency will fall or machine sets halt, even catastrophic accidents will occur. It is necessary and important to monitor key equipments and diagnose their faults in order to improve safety, allow predictive maintenance and shorten significantly the associated out of service time.
Equipment performance degradation is a continuous process, and there are several stages from the initial degradation to the final failure. If the degree of the equipment performance degradation can be detected, it would be possible to make credible maintenance schedule and prevent the urgent broken. Equipment performance degradation assessment and prediction is proposed based on the above idea. Unlike the traditional fault diagnosis research, whose aim is to fault type classification, the emphases of this research is the equipment degradation degree and trend. By taking rolling element bearing and rotor as the research basis, several degradation assessment and prediction methods are brought. The contents are as follows:
From the viewpoint of theoretical analysis and engineering application, the background and significance of the present study are elucidated. The state of the art review on equipment degradation assessment and prediction technology is thoroughly completed, respectively. The concrete research points are decided, then the research content of this paper are defined.
The principles of kernel analysis method are briefly talked about. The wavelet kernel is inducted to several kernel analysis methods, such as support vector machine, support vector regression and kernel principle component analysis. Based on the translation invariant kernel Mercer theorem, the admissible wavelet kernel is constructed based on Mexican hat mother wavelet. Utilizing emulation and testing data set, the generalization ability of kernel analysis method using wavelet kernel and the one using RBF kernel are compared. The results indicate that the former has better performance.
The definition of cyclostationarity entropy is brought forward as a monitoring tool according to CS characters of rolling element bearing. cyclostationarity entropy reflects the correlation between spectral lines, which will change with the development of failure and the deterioration of machine’s operation situation. Wavelet kernel principle analysis (WKPCA) method is used to reduce the dimension of characteristic vector. WKPCA can improve the assessment efficiency and veracity. By using testing bearing data set, the performance of cyclostationarity entropy feature extraction method and WKPCA feature reducing method are verified.
The degradation assessment method based on SVM multi-classifier is proposed. By utilizing the SVM binary tree algorithm, the decline problem of SVM based on“one-against-one”or“one-against-other”strategy is solved. The classification accuracy is improved by using the wavelet kernel. In order to increase the SVM training efficiency, the geometric distance probability parameters optimization is proposed. By using bearing data set with different pitting diameter, the performance of SVM assessment method and parameters optimization method are verified.
The degradation assessment method based on SVM geometric distance is proposed. During solving the binary classification problem, SVM algorithm will construct an optimal hyperplan. The geometric distance between the vector and the hyperplan is taken as the measurement of degradation degree. Using the cracked rotor emulation data sets, the generalization of this assessment method is studied. Based on Pauta Criterion, the self-adapting alarm technology is studied.
The performance degradation prediction method based on wavelet kernel support vector auto regression (WSVAR) is proposed. The inferences of different parameters on the prediction result are discussed. By using the binary grid searching method, the optimum prediction parameters are selected. By using bearing accelerated life testing data set, the prediction results of WSVAR and RBF-ANN and RBF-SVAR are compared. The results indicate that the WSVAR has more prediction accuracy.
A bearing accelerated life test is performed on the accelerated bearing life tester (ABLT-1A) and several bearing data sets are collected. The effective of these above feature extraction method, the performance degradation assessment method and degradation prediction method are validated.
A whole equipment performance degradation assessment system model based on analytic hierarchy process is proposed. And the system is developed based on the distributed programming model, WCF (Windows Communication Foundation), which is the realization technology of SOA (Service-Oriented Architecture). The utilization of WCF can solve the problem that the realization of performance degradation assessment algorithm is too complex to be satisfied with a single computer. A gas blower performance degradation assessment system is realized on the .NET platform, which validates the availability of the design.
设备从性能开始恶化到完全失效,要经过一个性能逐步退化的过程。如果能够在设备性能退化的过程中检测到设备性能退化的程度,那么就可以有针对地组织生产和设备维修,防止设备异常失效的发生。设备性能退化评估与预测正是基于以上思想提出的一种主动设备维护的技术。设备性能退化评估与预测侧重于对设备性能衰退状态全过程的走向预测,而不在于某个时间点的性能状态诊断,因此,其与现有的故障诊断技术在理念上和方法上都有很大的不同。本文以旋转机械的典型零部件为对象,深入开展了设备性能退化智能评估和预测的理论体系和技术方法研究,包括以下几个方面的内容:
从理论分析与工程应用的角度出发,阐述了论文的选题背景和研究意义。分析了设备性能退化评估方法、预测方法以及核模式分析方法等方面的国内外发展现状,总结了目前研究中需要解决的问题,确立了本论文的研究内容。
简要地叙述了核模式分析方法的原理,将小波核函数引入核模式分析方法中,研究了小波核函数在支持向量机分类器、支持向量机回归分析以及核主元分析中的应用。基于平移不变核函数Mercer条件,推导证明了Mexican hat小波函数构造的容许小波核函数。利用仿真数据和轴承试验数据,对比了小波核模式分析方法与RBF核模式分析方法的泛化能力,分析结果表明前者具有更好的性能。
提出了循环平稳熵分析方法,通过对轴承振动信号的相关性进行定量分析,准确地监测轴承运行状态。此外利用小波核函数核主元分析方法对多维特征向量进行约简,在全面掌握设备性能的同时,提高分析的效率并增强评估的准确性。利用轴承的试验数据对特征提取和特征约简方法进行了验证。
研究了基于支持向量机的多分类器性能退化评估方法。本文应用支持向量机二叉树算法进行设备性能退化评估研究,避免了传统支持向量机多分类器的拒识区域问题。将小波核函数引入支持向量机多分类器中,提高了分类器的分类精度。提出了几何距离概率统计准则,在保证分类精度的同时,提高支持向量机多分类器参数优选的效率。使用不同故障程度的轴承试验数据对核参数优化准则及分类算法进行了验证。
研究了基于支持向量机几何距离的性能退化评估模型。支持向量机算法在处理二分类问题时,首先利用核函数将特征向量映射到高维特征空间,然后在该特种空间中建立线性分类面进行分类。本文提出的评估方法利用设备状态特征向量与支持向量机最优分类面之间的几何距离,描述设备偏离正常状态的程度,从而实现对设备性能退化程度的定量分析。利用裂纹转子仿真模型,对评估模型的泛化推广能力进行了分析。根据拉依达准则,研究了连续变量的自适应报警阈值设定问题。裂纹转子和轴承疲劳试验数据验证了该评估方法的评估效果。
研究了基于小波核支持向量机自回归(WSVAR)模型的性能退化预测方法。研究了模型各个参数对预测结果的影响,并以二进制网格搜索方法选定模型最优参数。利用轴承加速疲劳试验全过程数据,对于WSVAR模型的预测效果进行了验证。并将WSVAR的预测结果分别与RBF核函数SVAR和RBF神经网络的预测结果进行了对比,表明WSVAR预测模型具有更高的预测精度。
进行了轴承强化疲劳实验研究。在轴承加速疲劳试验台上采集了多组轴承强化疲劳试验全过程数据,利用大量的滚动轴承信号验证了文中提出的各种特征提取方法、特征约简方法、性能退化评估和预测方法的适用性。
针对机械设备功能层次分级且相对独立的特点,提出了基于层次分析的设备整体性能退化评估系统模型。考虑系统计算量大及系统后续扩展性等问题,在系统的开发中引入了面向服务(SOA)技术,并基于SOA的成熟实现技术WCF开发了设备性能退化评估与预测系统原型。在某风机监测系统的现场应用验证了系统设计的有效性。
The rapid growth of technologies and market competition has already had a significant impact on commercial manufacture. The equipments development direction has some new characteristics, such as hugeness, distribution, high speed, automation and complexity. And also the equipments must face more and more harsh running condition. Once there is something wrong with them, production efficiency will fall or machine sets halt, even catastrophic accidents will occur. It is necessary and important to monitor key equipments and diagnose their faults in order to improve safety, allow predictive maintenance and shorten significantly the associated out of service time.
Equipment performance degradation is a continuous process, and there are several stages from the initial degradation to the final failure. If the degree of the equipment performance degradation can be detected, it would be possible to make credible maintenance schedule and prevent the urgent broken. Equipment performance degradation assessment and prediction is proposed based on the above idea. Unlike the traditional fault diagnosis research, whose aim is to fault type classification, the emphases of this research is the equipment degradation degree and trend. By taking rolling element bearing and rotor as the research basis, several degradation assessment and prediction methods are brought. The contents are as follows:
From the viewpoint of theoretical analysis and engineering application, the background and significance of the present study are elucidated. The state of the art review on equipment degradation assessment and prediction technology is thoroughly completed, respectively. The concrete research points are decided, then the research content of this paper are defined.
The principles of kernel analysis method are briefly talked about. The wavelet kernel is inducted to several kernel analysis methods, such as support vector machine, support vector regression and kernel principle component analysis. Based on the translation invariant kernel Mercer theorem, the admissible wavelet kernel is constructed based on Mexican hat mother wavelet. Utilizing emulation and testing data set, the generalization ability of kernel analysis method using wavelet kernel and the one using RBF kernel are compared. The results indicate that the former has better performance.
The definition of cyclostationarity entropy is brought forward as a monitoring tool according to CS characters of rolling element bearing. cyclostationarity entropy reflects the correlation between spectral lines, which will change with the development of failure and the deterioration of machine’s operation situation. Wavelet kernel principle analysis (WKPCA) method is used to reduce the dimension of characteristic vector. WKPCA can improve the assessment efficiency and veracity. By using testing bearing data set, the performance of cyclostationarity entropy feature extraction method and WKPCA feature reducing method are verified.
The degradation assessment method based on SVM multi-classifier is proposed. By utilizing the SVM binary tree algorithm, the decline problem of SVM based on“one-against-one”or“one-against-other”strategy is solved. The classification accuracy is improved by using the wavelet kernel. In order to increase the SVM training efficiency, the geometric distance probability parameters optimization is proposed. By using bearing data set with different pitting diameter, the performance of SVM assessment method and parameters optimization method are verified.
The degradation assessment method based on SVM geometric distance is proposed. During solving the binary classification problem, SVM algorithm will construct an optimal hyperplan. The geometric distance between the vector and the hyperplan is taken as the measurement of degradation degree. Using the cracked rotor emulation data sets, the generalization of this assessment method is studied. Based on Pauta Criterion, the self-adapting alarm technology is studied.
The performance degradation prediction method based on wavelet kernel support vector auto regression (WSVAR) is proposed. The inferences of different parameters on the prediction result are discussed. By using the binary grid searching method, the optimum prediction parameters are selected. By using bearing accelerated life testing data set, the prediction results of WSVAR and RBF-ANN and RBF-SVAR are compared. The results indicate that the WSVAR has more prediction accuracy.
A bearing accelerated life test is performed on the accelerated bearing life tester (ABLT-1A) and several bearing data sets are collected. The effective of these above feature extraction method, the performance degradation assessment method and degradation prediction method are validated.
A whole equipment performance degradation assessment system model based on analytic hierarchy process is proposed. And the system is developed based on the distributed programming model, WCF (Windows Communication Foundation), which is the realization technology of SOA (Service-Oriented Architecture). The utilization of WCF can solve the problem that the realization of performance degradation assessment algorithm is too complex to be satisfied with a single computer. A gas blower performance degradation assessment system is realized on the .NET platform, which validates the availability of the design.
引文
[1]陈进,机械设备故障诊断技术及其应用.上海:上海交通大学出版社, 1999.
[2] J. Lee, Intelligent Maintenance Systems (IMS) Technologies, 2006.
[3] Djurdjanovic D, Lee J, Ni J. Watchdog Agent - an infotronics-based prognostics approach for product performance degradation assessment and prediction. Advanced Engineering Informatics 2003;17(3-4):109-125.
[4]蒋林,大型回转机械运行状态评价与预报方法的研究.博士论文西安:西安交通大学, 1996.
[5] Suarez EL, Duffy MJ, Gamache RN, Morris RN, Hess AJ. Jet engine life prediction systems integrated with prognostics health management. 2004 IEEE Aerospace Conference Proceedings, Vols 1-6 2004:3596-3602
[6] S. W. J, C. K. A, and G. A. Goodman, Wegman, Anna K, Joint Strike Fighter Prognostics and Health Management, AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, 34th, Cleveland, OH, July 13-15, 1998
[7] Sandborn P. A decision support model for determining the applicability of prognostic health management (PHM) approaches to electronic systems. Annual Reliability and Maintainability Symposium, 2005 Proceedings, 2005:422-427.
[8] Choi MY, Kang KK, Kim JW, Park HY. The real-time health monitoring system of a large structure based on non-destructive testing. Smart Nondestructive Evaluation and Health Monitoring of Structural and Biological Systems Ii, 2003;5047:386-391
[9] Just VE, Ricard B, Chatellier L, Fournier L, Haik P. Information processing techniques: A major asset for the aging and life management of key NPP components. Proceedings of the ASME Pressure Vessels and Piping Conference 2005, Vol 7 2005;7:49-64
[10] Lee J. Measurement of machine performance degradation using a neural network model. Computers in Industry 1996;30(3):193-209.
[11] Qiu H, Lee J, Lin J, Yu G. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. Journal of Sound and Vibration 2005;289(4-5):1066-1090.
[12] Huang RQ, Xi LF, Li XL, Liu CR, Qiu H, Lee J. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mechanical Systems and Signal Processing 2007;21(1):193-207.
[13] J. Lee, IMS Chinese Introduction. http://www.imscenter.net/Resources/
[14] Haji M, Toliyat HA. Pattern recognition - A technique for induction machines rotor fault detection eccentricity and broken bar fault. Conference Record of the 2001 IEEE Industry Applications Conference, Vols 1-4 2001:1572-1578
[15] Weber P, Jouffe L. Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN). Reliability Engineering & System Safety2006;91(2):149-162.
[16] Sheppard JW, Kaufman MA. A Bayesian approach to diagnosis and prognosis using built-in test. IEEE Transactions on Instrumentation and Measurement 2005;54(3):1003-1018.
[17]徐宾刚,屈梁生,陶肖明,转子故障贝叶斯诊断网络的研究,机械工程学报, 2004, 40(l): 66-72
[18]张周锁,李凌均,何正嘉,基于支持向量机的多故障分类器及应用,机械科学与技术, 2004, 23(5):536-539.
[19] Ribeiro B. Support vector machines for quality, monitoring in a plastic injection molding process. IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviews 2005;35(3):401-410.
[20] Yuan S, Ge M, Qiu H, Lee J, Xu YS. Intelligent diagnosis in electromechanical operation systems. 2004 IEEE International Conference on Robotics and Automation, Vols 1- 5, Proceedings 2004:2267-2272
[21]王红军,徐小力,张建民,设备状态趋势的SVM预示技术研究,机械科学与技术, 2006, 25(04): 379-381.
[22] Tax DMJ, Duin RPW. Support vector domain description. Pattern Recognition Letters 1999;20(11-13):1191-1199.
[23]胡桥,张周锁,何正嘉,机械设备运行状态智能评估研究,振动工程学报, 2004, 17(zl): 313-316.
[24] Smyth P. Hidden Markov models for fault detection in dynamic systems. Pattern Recognition 1994;27(1):149-164.
[25]冯长建,丁启全,吴昭同,混合SOM和HMM方法在旋转机械升速全过程故障诊断中的应用,中国机械工程, vol. 13, pp. 1711-1717, 2002.
[26] Ertunc HM, Loparo KA, Ocak H. Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs). International Journal of Machine Tools & Manufacture 2001;41(9):1363-1384.
[27] Atlas L, Ostendorf M, Bernard GD. Hidden Markov models for monitoring machining tool-wear. 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing, Proceedings, Vols I-Vi 2000:3887-3890
[28] Ocak H, Loparo KA. HMM-based fault detection and diagnosis scheme for rolling element bearings. Journal of Vibration and Acoustics-Transactions of the Asme 2005;127(4):299-306.
[29] Ocak H, Loparo KA. A new bearing fault detection and diagnosis scheme based on hidden Markov modeling of vibration signals. 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vols I-Vi, Proceedings 2001:3141-3144
[30] Chinnam RB, Baruah P. Autonomous diagnostics and prognostics through competitive learning driven HMM-based clustering. Proceedings of the International Joint Conference on Neural Networks 2003, Vols 1-4 2003:2466-2471
[31] Kwan C, Zhang X, Xu R, Haynes L. A novel approach to fault diagnostics andprognostics. 2003 IEEE International Conference on Robotics and Automation, Vols 1-3, Proceedings 2003:604-609
[32] Awadallah MA, Morcos MA. Application of AI tools in fault diagnosis of electrical machines and drives - An overview. IEEE Transactions on Energy Conversion 2003;18(2):245-251.
[33] Lou XS, Loparo KA. Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical Systems and Signal Processing 2004;18(5):1077-1095.
[34] Suh, Jaehong, On-line machinery health diagnosis and prognosis for predictive maintenance and quality assurance of equipment functioning, Ph.D dissertation, The Pennsylvania State University, 2001.
[35]刘良顺,魏立东,宋希庚,薛冬新,基于RBF神经网络的滚动轴承故障诊断方法,农业机械学报, 2006, 37(3): 163-165.
[36] Denoeux T. A k-nearest neighbor classification rule based on Dempster-Shafer theory. Systems, Man and Cybernetics, IEEE Transactions on 1995;25(5):804-813.
[37] Denoeux T. A neural network classifier based on Dempster-Shafer theory. IEEE Transactions on Systems Man and Cybernetics Part a-Systems and Humans 2000;30(2):131-150.
[38]朱永生,王成栋,张优云,基于证据加权调整方法的神经网络及其在故障诊断中的应用,机械工程学报, 2002, 38(6):. 66-71.
[39] Yang SK. An experiment of state estimation for predictive maintenance using Kalman filter on a DC motor. Reliability Engineering & System Safety 2002;75(1):103-111.
[40] Choi S, Li CJ. Model based spur gear failure prediction using gear diagnosis. Manufacturing Engineering and Materials Handling, 2005 Pts a and B 2005;16:819-823
[41] Howard I, Jia SX, Wang JD. The dynamic modeling of a spur gear in mesh including friction and a crack. Mechanical Systems and Signal Processing 2001;15(5):831-853.
[42] Wang WY. Towards dynamic model-based prognostics for transmission gears. Component and Systems Diagnostics, Prognostics, and Health Management Ii 2002;4733:157-167
[43]李明华,屈彦明,周孟戈,董明,严璋,基于多Agent及Petri网的变压器故障诊断系统,西安交通大学学报, 2006, 40(2): 223-227.
[44] Propes NC, Propes NC. A fuzzy Petri net based mode identification algorithm for fault diagnosis of complex systems. System Diagnosis and Prognosis: Security and Condition Monitoring Issues Iii 2003;5107:44-53
[45] Jiroveanu G, Boel RK. A distributed approach for fault detection and diagnosis based on Time Petri Nets. Mathematics and Computers in Simulation 2006;70(5-6):287-313.
[46]孙怀江,杨景宇,基于证据理论的多分类器融合方法研究,计算机学报, vol. 24, pp. 231-235, 2001.
[47]寇忠宝,张长水,基于Multi-Agent的分类器融合,计算机学报, 2003, 26(2): 174-179.
[48] Niu G, Han T, Yang BS, Tan ACC. Multi-agent decision fusion for motor fault diagnosis. Mechanical Systems and Signal Processing 2007;21(3):1285-1299.
[49] Duin RPW, Tax DMJ. Experiments with classifier combining rules. Multiple Classifier Systems 2000;1857:16-29.
[50] Vapnik VN., An overview of statistical learning theory. Neural Networks, IEEE Transactions on 1999;10(5):988-999.
[51] Vapnik VN., The nature of statistical learning theory, Berlin: Springer-Verlag, 1995.
[52] B. Sch?lkopf, Christopher J. C. Burges,Alexander J. Smola, Advances in kernel methods: support vector learning, The MIT Press, 1998
[53] N. Cristianini and J. Shawe-Taylor, An introduction to Support Vector Machines: Cambridge University Press, 2000.
[54] B. Sch?lkopf and A. J. Smola., Learning with Kernels -- Support Vector Machines, Regularization, Optimization, and Beyond, The MIT Press, 2002.
[55] Muller KR, Mika S, Ratsch G, Tsuda K, Scholkopf B. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 2001;12(2):181-201.
[56] B. E. Boser, I. M. Guyon, V. N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, 1992:144-152
[57] Scholkopf B, Smola AJ, Williamson RC, Bartlett PL. New support vector algorithms. Neural Computation 2000;12(5):1207-1245.
[58] Burges CJC. A tutorial on Support Vector Machines for pattern recognition. Data Mining and Knowledge Discovery 1998;2(2):121-167.
[59] H. Drucker, C. J. C. Burges, L. Kaufman, A. Smola, and V. Vapnik, Support vector regression machines, in Advances in Neural Information Processing Systems 9, M. Mozer, M. Jordan, T. Petsche, Eds. Cambridge, MA: MIT Press, 1997:155-161.
[60] Osuna E, Freund R, Girosi F. An improved training algorithm for support vector machines. Neural Networks for Signal Processing Vii 1997:276-285
[61] E. Osuna and F. Girosi, Reducing run-time complexity in SVMs, in 14th International Conf. on Pattern Recognition, Brisbane, Australia, 1998.
[62] C. P. John, Fast training of support vector machines using sequential minimal optimization, in Advances in kernel methods: support vector learning: MIT Press, 1999: 185-208.
[63] Keerthi SS, Shevade SK, Bhattacharyya C, Murthy KRK. Improvements to Platt's SMO algorithm for SVM classifier design. Neural Computation 2001;13(3):637-649.
[64] Arenas-Garcia J, Perez-Cruz F. Multi-class Support Vector Machines: A new approach. 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol Ii, Proceedings 2003:781-784
[65] Bredensteiner EJ, Bennett KP. Multicategory Classification by Support Vector Machines. Computational Optimization and Applications 1999;12(1):53-79.
[66] Y. Guermeur, A. Elisseeff, and H. Paugam-Moisy, A new multiclass SVM based on a uniform convergence result, IJCNN'2000, 2000.
[67] Y. Lee, Y. Lin, and G. Wahba, Multi category Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satellite Radiance Data, Journal of the American Statistical Association, 2004, 465(99):67-81.
[68] Mika S, Ratsch G, Weston J, Scholkopf B, Mullers KR. Fisher discriminant analysis with kernels. 1999: 41-48.
[69] Park CH, Park H. Nonlinear discriminant analysis using kernel functions and the generalized singular value decomposition. Siam Journal on Matrix Analysis and Applications 2005;27(1):87-102.
[70] Baudat G, Anouar FE. Generalized discriminant analysis using a kernel approach. Neural Computation 2000;12(10):2385-2404.
[71] N. Ancona, Properties of support vector machines for regression, Technical Report, 1999.
[72] Smola AJ, Bartlett P. Sparse greedy Gaussian process regression. Advances in Neural Information Processing Systems 13 2001;13:619-625
[73] G. Wahba, An introduction to model buiIding with reproducing kernel hilbert spaces, University of Wisconsin-Madison, Statistics Dept. 2000.
[74] G. Wahba, Y. Lin, and H. Zhang, Gacv for support vector machines, in Advances in Large Margin Classifiers, Cambridge, MA: MIT Press, 2000:297-311.
[75] Zhang L, Zhou WD, Jiao LC. Wavelet support vector machine. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics 2004;34(1):34-39.
[76]张莉,周伟达,焦李成,尺度核函数支撑矢量机,电子学报, 2002, 30(4):527-529.
[77] Scholkopf B, Smola A, Muller KR. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 1998;10(5):1299-1319.
[78] B. Sch?lkopf, A. Smola, and K.-R. Müller, Kernel principal component analysis, in Artificial Neural Networks—ICANN'97, 583-588. 1997
[79] Girolami M. Orthogonal series density estimation and the kernel eigenvalue problem. Neural Computation 2002;14(3):669-688.
[80] Chen HX, Chua PSK, Lim CH. Fault degradation assessment of water hydraulic motor by impulse vibration signal with Wavelet Packet Analysis and Kolmogorov-Smirnov Test. Mechanical Systems and Signal Processing 2008;22(7):1670-1684.
[81] Chen HX, Chua PSK, Lim GH. Fault degradation assessment of water hydraulic components by noise signal with wavelet packet analysis. Journal of Testing and Evaluation 2008;36(5):464-472.
[82] Wu J, Schnettler A. Degradation assessment of nanostructured superhydrophobic insulating surfaces using multi-stress methods. IEEE Transactions on Dielectrics and Electrical Insulation 2008;15(1):73-80.
[83] Matuzas V, Uspuras E, Augutis J. Degradation assessment and management of systems with dependent components. Risk, Reliability and Societal Safety, Vols 1-3 2007:2313-2316
[84] Akturk MS, Gurel S. Machining conditions-based preventive maintenance. International Journal of Production Research 2007;45(8):1725-1743.
[85] V. Matuzas, J. Augutis, and E. Uspuras, Degradation assessment in complex systems, in International Conference on Nuclear Engineering, Proceedings, ICONE, Miami, FL, 2006.
[86] Jia ZD, Hao YP. The degradation assessment of epoxy/mica insulation under multi-stresses aging. IEEE Transactions on Dielectrics and Electrical Insulation 2006;13(2):415-422.
[87] Qiu H, Liao HT, Lee J. Degradation assessment for machinery prognostics using hidden Markov models. Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol 1, Pts A-C 2005:531-537
[88] Yan JH, Lee J. Degradation assessment and fault modes classification using logistic regression. Journal of Manufacturing Science and Engineering-Transactions of the Asme 2005;127(4):912-914.
[89] B. E. Boser, I. M. Guyon, and V. N. Vapnik, Training algorithm for optimal margin classifiers, in Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, Pittsburgh, PA, USA,144-152, 1992
[90]边肇棋张学工,模式识别.北京:清华大学出版社, 2000.
[91]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
[92] Mercer J. Functions of Positive and Negative Type, and Their Connection with the Theory of Integral Equations. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character 1909 ,83(559) :69-70.
[93] John Shawe-Taylor, Nello Cristianini, Kernel Methods for Pattern Aanlysis. Cambridge, England: Cambridge University Press, 2004.
[94] Burges CJC. A tutorial on Support Vector Machines for pattern recognition. Data Mining and Knowledge Discovery 1998;2(2):121-167.
[95] Cao LJ, Chua KS, Chong WK, Lee HP, Gu QM. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing 2003;55(1-2):321-336.
[96] Scholkopf B, Smola A, Muller KR. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 1998;10(5):1299-1319.
[97]赵松年熊小芸,子波变换与子波分析.北京:电子工业出版社, 1997.
[98] Smola AJ, Scholkopf B, Muller KR. The connection between regularization operators and support vector kernels. Neural Networks 1998;11(4):637-649.
[99] Gardner W. Stationarizable random processes. Information Theory, IEEE Transactions on 1978;24(1):8-22.
[100]毕果,循环平稳分析在滚动轴承及齿轮微弱特征识别上的研究,博士论文上海:上海交通大学,.2007.
[101] G. D. Zivanovic Gardner WA. Degrees of cyclostationarity and their application to signal detection and estimation. Signal Processing 1991;22(3):287-297.
[102] C.E.SHANNON, A mathematical theory of communication, The Bell System Technical Journal, 1948,27(623-656): 379-433
[103]申弢,黄树红,韩守木,杨叔子,旋转机械振动信号的信息熵特征,机械工程学报, 2001, 37(6): 94-98.
[104]何正友,符玲,麦瑞坤,钱清泉,张鹏,小波奇异熵及其在高压输电线路故障选相中的应用,中国电机工程学报, 2007, 27(1): 31-36.
[105] Yu DJ, Yang Y, Cheng JS. Application of time-frequency entropy method based on Hilbert-Huang transform to gear fault diagnosis. Measurement 2007;40(9-10):823-830.
[106] Endo H, Randall RB. Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter. Mechanical Systems and Signal Processing 2007;21(2):906-919.
[107] Li H, Zhou P, Ma X. Pattern recognition on diesel engine working condition by using a novel methodology - Hubert spectrum entropy. Proceedings of the Institute of Marine Engineering, Science and Technology Part A: Journal of Marine Engineering and Technology 2005(6):43-48.
[108] Starzyk JA, Liu D, Liu ZH, Nelson DE, Rutkowski JO. Entropy-based optimum test points selection for analog fault dictionary techniques. IEEE Transactions on Instrumentation and Measurement 2004;53(3):754-761.
[109] Seong PH, Golay MW, Manno VP. Diagnostic entropy: a quantitative measure of the effects of signal incompleteness on system diagnosis. Reliability Engineering and System Safety 1994;45(3):235-248.
[110] Xiaozhong W. Fault tree diagnosis based on shannon entropy. Reliability Engineering and System Safety 1991;34(2):143-167.
[111] Gardner WA, Napolitano A, Paura L. Cyclostationarity: Half a century of research. Signal Processing 2006;86(4):639-697.
[112] Chen C-K, Gardner WA. Signal-selective time-difference of arrival estimation for passive location of man-made signal sources in highly corruptive environments--II: Algorithms and performance. IEEE Transactions on Signal Processing 1992;40(5):1185-1197.
[113] Gardner WA, Spooner CM. Cumulant theory of cyclostationary time-series, Part I: Foundation. IEEE Transactions on Signal Processing 1994;42(12):3387-3408.
[114] Spooner CM, Gardner WA. Cumulant theory of cyclostationary time series, Part II: Development and applications. IEEE Transactions on Signal Processing 1994;42(12):3409-3429.
[115] Gardner WA, Spooner CM. Detection and source location of weak cyclostationary signals: simplifications of maximum-likelihood receiver. IEEE Transactions onCommunications 1993;41(6):905-916.
[116] Gardner WA, Spooner CM. Signal interception: Performance advantages of cyclic-feature detectors. IEEE Transactions on Communications 1992;40(1):149-159.
[117] Gardner WA. Signal interception: A unifying theoretical framework for feature detection. IEEE Transactions on Communications 1988;36(8):897-906.
[118] W. A. Gardner, SPECTRAL CORRELATION OF MODULATED SIGNALS:I - ANALOG MODULATION, IEEE Transactions on Communications, vol. COM-35, pp. 584-594, 1987.
[119] Gardner WA. COMMON PITFALLS IN THE APPLICATION OF STATIONARY PROCESS THEORY TO TIME-SAMPLED AND MODULATED SIGNALS. IEEE Transactions on Communications 1987;COM-35(5):529-534.
[120] Gardner WA. The spectral correlation theory of cyclostationary time-series. Signal processing vol. 11, no. 1, July 1986, pp. 13-36. Signal Processing 1986;11(4):405.
[121] Xie XD, Lam KM. Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image. IEEE Transactions on Image Processing 2006;15(9):2481-2492.
[122] Wu FF, Zhao YL. A novel reduced support vector machine on Morlet wavelet kernel function. Proceedings of the 11th Joint International Computer Conference 2005:348-351
[123] Skobelev SP. Application of extended boundary conditions and the Haar wavelets in the analysis of wave scattering by thin screens. Journal of Communications Technology and Electronics 2006;51(7):748-758.
[124] Wu F, Zhao Y. Least squares support vector machine on Morlet wavelet kernel function and its application to nonlinear system identification. Information Technology Journal 2006;5(3):438-444.
[125] Lin W, Zheng T, He F, Wen XB. The cook projection index estimation using the wavelet Kernel function. Advances in Neural Networks - Isnn 2004, Pt 1 2004;3173:822-827.
[126] Wang H, Zeng Y, Zhao Z, Wang C. Classifying data sets using posterior probability for multiclass support vector machines. Journal of Computational Information Systems 2008;4(2):541-546.
[127] Wu F, Zhao Y. Least squares support vector machine on Morlet wavelet kernel function and its application to nonlinear system identification. Information Technology Journal 2006;5(3):438-444.
[128] J. Weston and C. Watkins, Multi-class support vector machine, 1998.
[129]黄勇,郑春颖,宋忠虎,多类支持向量机算法综述,计算技术与自动化, 2005, 24(4): 61-63.
[130]邹剑,具有横向裂纹转子的振动特性与无损检测.博士论文上海:上海交通大学, 2004.
[131] Gao CW, Bompard E, Napoli R, Cheng HZ. Price forecast in the competitiveelectricity market by support vector machine. Physica a-Statistical Mechanics and Its Applications 2007;382(1):98-113.
[132] Lee JJ, Kim DK, Chang SK, Lee JH. Application of support vector regression for the prediction of concrete strength. Computers and Concrete 2007;4(4):299-316.
[133] Ma YJ. Research on soft sensor modeling of fermentation process based on nu-SVR. 2006 IEEE International Conference on Information Acquisition, Vols 1 and 2, Conference Proceedings 2006:759-763
[134] Zheng DN, Wang JX, Zhao YN. Time series predictions using multi-scale support vector regressions. Theory and Applications of Models of Computation, Proceedings 2006;3959:474-481.
[135]朱嘉钢,王士同,杨静宇,鲁棒r-支持向量回归机中参数r的选择研究,控制与决策, 2004, 19(12): 1383-1386.
[136] Chang MW, Lin CJ. Leave-one-out bounds for support vector regression model selection. Neural Computation 2005;17(5):1188-1222.
[137] C. H. Li, Z. D. Lu, and K. Zhou, SVR-parameters selection for image watermarking, in Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, Hong Kong, 2005, pp. 466-470.
[138] Li CH, Lu ZD, Zhou K. SVR-parameters selection for image watermarking. ICTAI 2005: 17th IEEE International Conference on Tools with Artificial Intelligence, Proceedings 2005:466-470
[139]杜京义,侯媛彬,基于遗传算法的支持向量回归机参数选取,系统工程与电子技术, 2006, 28(9): 1430-1433.
[140] Bo W, Wang XQ, Bo H. Application of adaptive kernel matching pursuit to estimate mixture pixel proportion. Proceedings of the Fourth International Conference on Image and Graphics 2007:542-547
[141] Cherkassky V, Ma YQ. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 2004;17(1):113-126.
[142]吴怀宇,时间序列分析与综合.武汉:武汉大学出版社, 2004.
[143] Bae YH, Lee SH, Kim HC, Lee BR, Jang J, Lee J. A real-time intelligent multiple fault diagnostic system. International Journal of Advanced Manufacturing Technology 2006;29(5):590-597.
[144]安若铭,姜兴渭,宋政吉,基于功能单元的分层诊断技术研究及在航天器中的应用,宇航学报, 2005, 26(4): 519-523.
[145] Xu L, Hsiang SM, Chow MY. The development of fault diagnosis methodologies using hierarchical clustering and small world network stratification. Proceedings of the 2006 IEEE Mountain Workshop on Adaptive and Learning Systems 2006:149-153
[146] Burns CM. Putting it all together: Improving display integration in ecological displays. Human Factors 2000;42(2):226-241.
[147] Xu XL, Tong SR, Li B. A conceptual model of manufacturing quality information based on product structure tree. 2007 International Conference on WirelessCommunications, Networking and Mobile Computing, WiCOM 2007; 2007; Shanghai. p 5124-5127.
[148] Perrey R, Lycett M. Service-oriented architecture. 2003 Symposium on Applications and the Internet Workshops, Proceedings 2003:116-119
[149] Huhns MN, Singh MP. Service-oriented computing: Key concepts and principles. IEEE Internet Computing 2005;9(1):75-81.
[150] Microsoft, What Is Windows Communication Foundation? http://msdn.microsoft.com/zh-cn/library/aa480210.aspx
[151] Microsoft,如何在IIS中承载WCF服务. http://msdn.microsoft.com/zh-cn/library/ms733766.aspx
[2] J. Lee, Intelligent Maintenance Systems (IMS) Technologies, 2006.
[3] Djurdjanovic D, Lee J, Ni J. Watchdog Agent - an infotronics-based prognostics approach for product performance degradation assessment and prediction. Advanced Engineering Informatics 2003;17(3-4):109-125.
[4]蒋林,大型回转机械运行状态评价与预报方法的研究.博士论文西安:西安交通大学, 1996.
[5] Suarez EL, Duffy MJ, Gamache RN, Morris RN, Hess AJ. Jet engine life prediction systems integrated with prognostics health management. 2004 IEEE Aerospace Conference Proceedings, Vols 1-6 2004:3596-3602
[6] S. W. J, C. K. A, and G. A. Goodman, Wegman, Anna K, Joint Strike Fighter Prognostics and Health Management, AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, 34th, Cleveland, OH, July 13-15, 1998
[7] Sandborn P. A decision support model for determining the applicability of prognostic health management (PHM) approaches to electronic systems. Annual Reliability and Maintainability Symposium, 2005 Proceedings, 2005:422-427.
[8] Choi MY, Kang KK, Kim JW, Park HY. The real-time health monitoring system of a large structure based on non-destructive testing. Smart Nondestructive Evaluation and Health Monitoring of Structural and Biological Systems Ii, 2003;5047:386-391
[9] Just VE, Ricard B, Chatellier L, Fournier L, Haik P. Information processing techniques: A major asset for the aging and life management of key NPP components. Proceedings of the ASME Pressure Vessels and Piping Conference 2005, Vol 7 2005;7:49-64
[10] Lee J. Measurement of machine performance degradation using a neural network model. Computers in Industry 1996;30(3):193-209.
[11] Qiu H, Lee J, Lin J, Yu G. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. Journal of Sound and Vibration 2005;289(4-5):1066-1090.
[12] Huang RQ, Xi LF, Li XL, Liu CR, Qiu H, Lee J. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mechanical Systems and Signal Processing 2007;21(1):193-207.
[13] J. Lee, IMS Chinese Introduction. http://www.imscenter.net/Resources/
[14] Haji M, Toliyat HA. Pattern recognition - A technique for induction machines rotor fault detection eccentricity and broken bar fault. Conference Record of the 2001 IEEE Industry Applications Conference, Vols 1-4 2001:1572-1578
[15] Weber P, Jouffe L. Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN). Reliability Engineering & System Safety2006;91(2):149-162.
[16] Sheppard JW, Kaufman MA. A Bayesian approach to diagnosis and prognosis using built-in test. IEEE Transactions on Instrumentation and Measurement 2005;54(3):1003-1018.
[17]徐宾刚,屈梁生,陶肖明,转子故障贝叶斯诊断网络的研究,机械工程学报, 2004, 40(l): 66-72
[18]张周锁,李凌均,何正嘉,基于支持向量机的多故障分类器及应用,机械科学与技术, 2004, 23(5):536-539.
[19] Ribeiro B. Support vector machines for quality, monitoring in a plastic injection molding process. IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviews 2005;35(3):401-410.
[20] Yuan S, Ge M, Qiu H, Lee J, Xu YS. Intelligent diagnosis in electromechanical operation systems. 2004 IEEE International Conference on Robotics and Automation, Vols 1- 5, Proceedings 2004:2267-2272
[21]王红军,徐小力,张建民,设备状态趋势的SVM预示技术研究,机械科学与技术, 2006, 25(04): 379-381.
[22] Tax DMJ, Duin RPW. Support vector domain description. Pattern Recognition Letters 1999;20(11-13):1191-1199.
[23]胡桥,张周锁,何正嘉,机械设备运行状态智能评估研究,振动工程学报, 2004, 17(zl): 313-316.
[24] Smyth P. Hidden Markov models for fault detection in dynamic systems. Pattern Recognition 1994;27(1):149-164.
[25]冯长建,丁启全,吴昭同,混合SOM和HMM方法在旋转机械升速全过程故障诊断中的应用,中国机械工程, vol. 13, pp. 1711-1717, 2002.
[26] Ertunc HM, Loparo KA, Ocak H. Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs). International Journal of Machine Tools & Manufacture 2001;41(9):1363-1384.
[27] Atlas L, Ostendorf M, Bernard GD. Hidden Markov models for monitoring machining tool-wear. 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing, Proceedings, Vols I-Vi 2000:3887-3890
[28] Ocak H, Loparo KA. HMM-based fault detection and diagnosis scheme for rolling element bearings. Journal of Vibration and Acoustics-Transactions of the Asme 2005;127(4):299-306.
[29] Ocak H, Loparo KA. A new bearing fault detection and diagnosis scheme based on hidden Markov modeling of vibration signals. 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vols I-Vi, Proceedings 2001:3141-3144
[30] Chinnam RB, Baruah P. Autonomous diagnostics and prognostics through competitive learning driven HMM-based clustering. Proceedings of the International Joint Conference on Neural Networks 2003, Vols 1-4 2003:2466-2471
[31] Kwan C, Zhang X, Xu R, Haynes L. A novel approach to fault diagnostics andprognostics. 2003 IEEE International Conference on Robotics and Automation, Vols 1-3, Proceedings 2003:604-609
[32] Awadallah MA, Morcos MA. Application of AI tools in fault diagnosis of electrical machines and drives - An overview. IEEE Transactions on Energy Conversion 2003;18(2):245-251.
[33] Lou XS, Loparo KA. Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical Systems and Signal Processing 2004;18(5):1077-1095.
[34] Suh, Jaehong, On-line machinery health diagnosis and prognosis for predictive maintenance and quality assurance of equipment functioning, Ph.D dissertation, The Pennsylvania State University, 2001.
[35]刘良顺,魏立东,宋希庚,薛冬新,基于RBF神经网络的滚动轴承故障诊断方法,农业机械学报, 2006, 37(3): 163-165.
[36] Denoeux T. A k-nearest neighbor classification rule based on Dempster-Shafer theory. Systems, Man and Cybernetics, IEEE Transactions on 1995;25(5):804-813.
[37] Denoeux T. A neural network classifier based on Dempster-Shafer theory. IEEE Transactions on Systems Man and Cybernetics Part a-Systems and Humans 2000;30(2):131-150.
[38]朱永生,王成栋,张优云,基于证据加权调整方法的神经网络及其在故障诊断中的应用,机械工程学报, 2002, 38(6):. 66-71.
[39] Yang SK. An experiment of state estimation for predictive maintenance using Kalman filter on a DC motor. Reliability Engineering & System Safety 2002;75(1):103-111.
[40] Choi S, Li CJ. Model based spur gear failure prediction using gear diagnosis. Manufacturing Engineering and Materials Handling, 2005 Pts a and B 2005;16:819-823
[41] Howard I, Jia SX, Wang JD. The dynamic modeling of a spur gear in mesh including friction and a crack. Mechanical Systems and Signal Processing 2001;15(5):831-853.
[42] Wang WY. Towards dynamic model-based prognostics for transmission gears. Component and Systems Diagnostics, Prognostics, and Health Management Ii 2002;4733:157-167
[43]李明华,屈彦明,周孟戈,董明,严璋,基于多Agent及Petri网的变压器故障诊断系统,西安交通大学学报, 2006, 40(2): 223-227.
[44] Propes NC, Propes NC. A fuzzy Petri net based mode identification algorithm for fault diagnosis of complex systems. System Diagnosis and Prognosis: Security and Condition Monitoring Issues Iii 2003;5107:44-53
[45] Jiroveanu G, Boel RK. A distributed approach for fault detection and diagnosis based on Time Petri Nets. Mathematics and Computers in Simulation 2006;70(5-6):287-313.
[46]孙怀江,杨景宇,基于证据理论的多分类器融合方法研究,计算机学报, vol. 24, pp. 231-235, 2001.
[47]寇忠宝,张长水,基于Multi-Agent的分类器融合,计算机学报, 2003, 26(2): 174-179.
[48] Niu G, Han T, Yang BS, Tan ACC. Multi-agent decision fusion for motor fault diagnosis. Mechanical Systems and Signal Processing 2007;21(3):1285-1299.
[49] Duin RPW, Tax DMJ. Experiments with classifier combining rules. Multiple Classifier Systems 2000;1857:16-29.
[50] Vapnik VN., An overview of statistical learning theory. Neural Networks, IEEE Transactions on 1999;10(5):988-999.
[51] Vapnik VN., The nature of statistical learning theory, Berlin: Springer-Verlag, 1995.
[52] B. Sch?lkopf, Christopher J. C. Burges,Alexander J. Smola, Advances in kernel methods: support vector learning, The MIT Press, 1998
[53] N. Cristianini and J. Shawe-Taylor, An introduction to Support Vector Machines: Cambridge University Press, 2000.
[54] B. Sch?lkopf and A. J. Smola., Learning with Kernels -- Support Vector Machines, Regularization, Optimization, and Beyond, The MIT Press, 2002.
[55] Muller KR, Mika S, Ratsch G, Tsuda K, Scholkopf B. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 2001;12(2):181-201.
[56] B. E. Boser, I. M. Guyon, V. N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, 1992:144-152
[57] Scholkopf B, Smola AJ, Williamson RC, Bartlett PL. New support vector algorithms. Neural Computation 2000;12(5):1207-1245.
[58] Burges CJC. A tutorial on Support Vector Machines for pattern recognition. Data Mining and Knowledge Discovery 1998;2(2):121-167.
[59] H. Drucker, C. J. C. Burges, L. Kaufman, A. Smola, and V. Vapnik, Support vector regression machines, in Advances in Neural Information Processing Systems 9, M. Mozer, M. Jordan, T. Petsche, Eds. Cambridge, MA: MIT Press, 1997:155-161.
[60] Osuna E, Freund R, Girosi F. An improved training algorithm for support vector machines. Neural Networks for Signal Processing Vii 1997:276-285
[61] E. Osuna and F. Girosi, Reducing run-time complexity in SVMs, in 14th International Conf. on Pattern Recognition, Brisbane, Australia, 1998.
[62] C. P. John, Fast training of support vector machines using sequential minimal optimization, in Advances in kernel methods: support vector learning: MIT Press, 1999: 185-208.
[63] Keerthi SS, Shevade SK, Bhattacharyya C, Murthy KRK. Improvements to Platt's SMO algorithm for SVM classifier design. Neural Computation 2001;13(3):637-649.
[64] Arenas-Garcia J, Perez-Cruz F. Multi-class Support Vector Machines: A new approach. 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol Ii, Proceedings 2003:781-784
[65] Bredensteiner EJ, Bennett KP. Multicategory Classification by Support Vector Machines. Computational Optimization and Applications 1999;12(1):53-79.
[66] Y. Guermeur, A. Elisseeff, and H. Paugam-Moisy, A new multiclass SVM based on a uniform convergence result, IJCNN'2000, 2000.
[67] Y. Lee, Y. Lin, and G. Wahba, Multi category Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satellite Radiance Data, Journal of the American Statistical Association, 2004, 465(99):67-81.
[68] Mika S, Ratsch G, Weston J, Scholkopf B, Mullers KR. Fisher discriminant analysis with kernels. 1999: 41-48.
[69] Park CH, Park H. Nonlinear discriminant analysis using kernel functions and the generalized singular value decomposition. Siam Journal on Matrix Analysis and Applications 2005;27(1):87-102.
[70] Baudat G, Anouar FE. Generalized discriminant analysis using a kernel approach. Neural Computation 2000;12(10):2385-2404.
[71] N. Ancona, Properties of support vector machines for regression, Technical Report, 1999.
[72] Smola AJ, Bartlett P. Sparse greedy Gaussian process regression. Advances in Neural Information Processing Systems 13 2001;13:619-625
[73] G. Wahba, An introduction to model buiIding with reproducing kernel hilbert spaces, University of Wisconsin-Madison, Statistics Dept. 2000.
[74] G. Wahba, Y. Lin, and H. Zhang, Gacv for support vector machines, in Advances in Large Margin Classifiers, Cambridge, MA: MIT Press, 2000:297-311.
[75] Zhang L, Zhou WD, Jiao LC. Wavelet support vector machine. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics 2004;34(1):34-39.
[76]张莉,周伟达,焦李成,尺度核函数支撑矢量机,电子学报, 2002, 30(4):527-529.
[77] Scholkopf B, Smola A, Muller KR. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 1998;10(5):1299-1319.
[78] B. Sch?lkopf, A. Smola, and K.-R. Müller, Kernel principal component analysis, in Artificial Neural Networks—ICANN'97, 583-588. 1997
[79] Girolami M. Orthogonal series density estimation and the kernel eigenvalue problem. Neural Computation 2002;14(3):669-688.
[80] Chen HX, Chua PSK, Lim CH. Fault degradation assessment of water hydraulic motor by impulse vibration signal with Wavelet Packet Analysis and Kolmogorov-Smirnov Test. Mechanical Systems and Signal Processing 2008;22(7):1670-1684.
[81] Chen HX, Chua PSK, Lim GH. Fault degradation assessment of water hydraulic components by noise signal with wavelet packet analysis. Journal of Testing and Evaluation 2008;36(5):464-472.
[82] Wu J, Schnettler A. Degradation assessment of nanostructured superhydrophobic insulating surfaces using multi-stress methods. IEEE Transactions on Dielectrics and Electrical Insulation 2008;15(1):73-80.
[83] Matuzas V, Uspuras E, Augutis J. Degradation assessment and management of systems with dependent components. Risk, Reliability and Societal Safety, Vols 1-3 2007:2313-2316
[84] Akturk MS, Gurel S. Machining conditions-based preventive maintenance. International Journal of Production Research 2007;45(8):1725-1743.
[85] V. Matuzas, J. Augutis, and E. Uspuras, Degradation assessment in complex systems, in International Conference on Nuclear Engineering, Proceedings, ICONE, Miami, FL, 2006.
[86] Jia ZD, Hao YP. The degradation assessment of epoxy/mica insulation under multi-stresses aging. IEEE Transactions on Dielectrics and Electrical Insulation 2006;13(2):415-422.
[87] Qiu H, Liao HT, Lee J. Degradation assessment for machinery prognostics using hidden Markov models. Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol 1, Pts A-C 2005:531-537
[88] Yan JH, Lee J. Degradation assessment and fault modes classification using logistic regression. Journal of Manufacturing Science and Engineering-Transactions of the Asme 2005;127(4):912-914.
[89] B. E. Boser, I. M. Guyon, and V. N. Vapnik, Training algorithm for optimal margin classifiers, in Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, Pittsburgh, PA, USA,144-152, 1992
[90]边肇棋张学工,模式识别.北京:清华大学出版社, 2000.
[91]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
[92] Mercer J. Functions of Positive and Negative Type, and Their Connection with the Theory of Integral Equations. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character 1909 ,83(559) :69-70.
[93] John Shawe-Taylor, Nello Cristianini, Kernel Methods for Pattern Aanlysis. Cambridge, England: Cambridge University Press, 2004.
[94] Burges CJC. A tutorial on Support Vector Machines for pattern recognition. Data Mining and Knowledge Discovery 1998;2(2):121-167.
[95] Cao LJ, Chua KS, Chong WK, Lee HP, Gu QM. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing 2003;55(1-2):321-336.
[96] Scholkopf B, Smola A, Muller KR. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 1998;10(5):1299-1319.
[97]赵松年熊小芸,子波变换与子波分析.北京:电子工业出版社, 1997.
[98] Smola AJ, Scholkopf B, Muller KR. The connection between regularization operators and support vector kernels. Neural Networks 1998;11(4):637-649.
[99] Gardner W. Stationarizable random processes. Information Theory, IEEE Transactions on 1978;24(1):8-22.
[100]毕果,循环平稳分析在滚动轴承及齿轮微弱特征识别上的研究,博士论文上海:上海交通大学,.2007.
[101] G. D. Zivanovic Gardner WA. Degrees of cyclostationarity and their application to signal detection and estimation. Signal Processing 1991;22(3):287-297.
[102] C.E.SHANNON, A mathematical theory of communication, The Bell System Technical Journal, 1948,27(623-656): 379-433
[103]申弢,黄树红,韩守木,杨叔子,旋转机械振动信号的信息熵特征,机械工程学报, 2001, 37(6): 94-98.
[104]何正友,符玲,麦瑞坤,钱清泉,张鹏,小波奇异熵及其在高压输电线路故障选相中的应用,中国电机工程学报, 2007, 27(1): 31-36.
[105] Yu DJ, Yang Y, Cheng JS. Application of time-frequency entropy method based on Hilbert-Huang transform to gear fault diagnosis. Measurement 2007;40(9-10):823-830.
[106] Endo H, Randall RB. Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter. Mechanical Systems and Signal Processing 2007;21(2):906-919.
[107] Li H, Zhou P, Ma X. Pattern recognition on diesel engine working condition by using a novel methodology - Hubert spectrum entropy. Proceedings of the Institute of Marine Engineering, Science and Technology Part A: Journal of Marine Engineering and Technology 2005(6):43-48.
[108] Starzyk JA, Liu D, Liu ZH, Nelson DE, Rutkowski JO. Entropy-based optimum test points selection for analog fault dictionary techniques. IEEE Transactions on Instrumentation and Measurement 2004;53(3):754-761.
[109] Seong PH, Golay MW, Manno VP. Diagnostic entropy: a quantitative measure of the effects of signal incompleteness on system diagnosis. Reliability Engineering and System Safety 1994;45(3):235-248.
[110] Xiaozhong W. Fault tree diagnosis based on shannon entropy. Reliability Engineering and System Safety 1991;34(2):143-167.
[111] Gardner WA, Napolitano A, Paura L. Cyclostationarity: Half a century of research. Signal Processing 2006;86(4):639-697.
[112] Chen C-K, Gardner WA. Signal-selective time-difference of arrival estimation for passive location of man-made signal sources in highly corruptive environments--II: Algorithms and performance. IEEE Transactions on Signal Processing 1992;40(5):1185-1197.
[113] Gardner WA, Spooner CM. Cumulant theory of cyclostationary time-series, Part I: Foundation. IEEE Transactions on Signal Processing 1994;42(12):3387-3408.
[114] Spooner CM, Gardner WA. Cumulant theory of cyclostationary time series, Part II: Development and applications. IEEE Transactions on Signal Processing 1994;42(12):3409-3429.
[115] Gardner WA, Spooner CM. Detection and source location of weak cyclostationary signals: simplifications of maximum-likelihood receiver. IEEE Transactions onCommunications 1993;41(6):905-916.
[116] Gardner WA, Spooner CM. Signal interception: Performance advantages of cyclic-feature detectors. IEEE Transactions on Communications 1992;40(1):149-159.
[117] Gardner WA. Signal interception: A unifying theoretical framework for feature detection. IEEE Transactions on Communications 1988;36(8):897-906.
[118] W. A. Gardner, SPECTRAL CORRELATION OF MODULATED SIGNALS:I - ANALOG MODULATION, IEEE Transactions on Communications, vol. COM-35, pp. 584-594, 1987.
[119] Gardner WA. COMMON PITFALLS IN THE APPLICATION OF STATIONARY PROCESS THEORY TO TIME-SAMPLED AND MODULATED SIGNALS. IEEE Transactions on Communications 1987;COM-35(5):529-534.
[120] Gardner WA. The spectral correlation theory of cyclostationary time-series. Signal processing vol. 11, no. 1, July 1986, pp. 13-36. Signal Processing 1986;11(4):405.
[121] Xie XD, Lam KM. Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image. IEEE Transactions on Image Processing 2006;15(9):2481-2492.
[122] Wu FF, Zhao YL. A novel reduced support vector machine on Morlet wavelet kernel function. Proceedings of the 11th Joint International Computer Conference 2005:348-351
[123] Skobelev SP. Application of extended boundary conditions and the Haar wavelets in the analysis of wave scattering by thin screens. Journal of Communications Technology and Electronics 2006;51(7):748-758.
[124] Wu F, Zhao Y. Least squares support vector machine on Morlet wavelet kernel function and its application to nonlinear system identification. Information Technology Journal 2006;5(3):438-444.
[125] Lin W, Zheng T, He F, Wen XB. The cook projection index estimation using the wavelet Kernel function. Advances in Neural Networks - Isnn 2004, Pt 1 2004;3173:822-827.
[126] Wang H, Zeng Y, Zhao Z, Wang C. Classifying data sets using posterior probability for multiclass support vector machines. Journal of Computational Information Systems 2008;4(2):541-546.
[127] Wu F, Zhao Y. Least squares support vector machine on Morlet wavelet kernel function and its application to nonlinear system identification. Information Technology Journal 2006;5(3):438-444.
[128] J. Weston and C. Watkins, Multi-class support vector machine, 1998.
[129]黄勇,郑春颖,宋忠虎,多类支持向量机算法综述,计算技术与自动化, 2005, 24(4): 61-63.
[130]邹剑,具有横向裂纹转子的振动特性与无损检测.博士论文上海:上海交通大学, 2004.
[131] Gao CW, Bompard E, Napoli R, Cheng HZ. Price forecast in the competitiveelectricity market by support vector machine. Physica a-Statistical Mechanics and Its Applications 2007;382(1):98-113.
[132] Lee JJ, Kim DK, Chang SK, Lee JH. Application of support vector regression for the prediction of concrete strength. Computers and Concrete 2007;4(4):299-316.
[133] Ma YJ. Research on soft sensor modeling of fermentation process based on nu-SVR. 2006 IEEE International Conference on Information Acquisition, Vols 1 and 2, Conference Proceedings 2006:759-763
[134] Zheng DN, Wang JX, Zhao YN. Time series predictions using multi-scale support vector regressions. Theory and Applications of Models of Computation, Proceedings 2006;3959:474-481.
[135]朱嘉钢,王士同,杨静宇,鲁棒r-支持向量回归机中参数r的选择研究,控制与决策, 2004, 19(12): 1383-1386.
[136] Chang MW, Lin CJ. Leave-one-out bounds for support vector regression model selection. Neural Computation 2005;17(5):1188-1222.
[137] C. H. Li, Z. D. Lu, and K. Zhou, SVR-parameters selection for image watermarking, in Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, Hong Kong, 2005, pp. 466-470.
[138] Li CH, Lu ZD, Zhou K. SVR-parameters selection for image watermarking. ICTAI 2005: 17th IEEE International Conference on Tools with Artificial Intelligence, Proceedings 2005:466-470
[139]杜京义,侯媛彬,基于遗传算法的支持向量回归机参数选取,系统工程与电子技术, 2006, 28(9): 1430-1433.
[140] Bo W, Wang XQ, Bo H. Application of adaptive kernel matching pursuit to estimate mixture pixel proportion. Proceedings of the Fourth International Conference on Image and Graphics 2007:542-547
[141] Cherkassky V, Ma YQ. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 2004;17(1):113-126.
[142]吴怀宇,时间序列分析与综合.武汉:武汉大学出版社, 2004.
[143] Bae YH, Lee SH, Kim HC, Lee BR, Jang J, Lee J. A real-time intelligent multiple fault diagnostic system. International Journal of Advanced Manufacturing Technology 2006;29(5):590-597.
[144]安若铭,姜兴渭,宋政吉,基于功能单元的分层诊断技术研究及在航天器中的应用,宇航学报, 2005, 26(4): 519-523.
[145] Xu L, Hsiang SM, Chow MY. The development of fault diagnosis methodologies using hierarchical clustering and small world network stratification. Proceedings of the 2006 IEEE Mountain Workshop on Adaptive and Learning Systems 2006:149-153
[146] Burns CM. Putting it all together: Improving display integration in ecological displays. Human Factors 2000;42(2):226-241.
[147] Xu XL, Tong SR, Li B. A conceptual model of manufacturing quality information based on product structure tree. 2007 International Conference on WirelessCommunications, Networking and Mobile Computing, WiCOM 2007; 2007; Shanghai. p 5124-5127.
[148] Perrey R, Lycett M. Service-oriented architecture. 2003 Symposium on Applications and the Internet Workshops, Proceedings 2003:116-119
[149] Huhns MN, Singh MP. Service-oriented computing: Key concepts and principles. IEEE Internet Computing 2005;9(1):75-81.
[150] Microsoft, What Is Windows Communication Foundation? http://msdn.microsoft.com/zh-cn/library/aa480210.aspx
[151] Microsoft,如何在IIS中承载WCF服务. http://msdn.microsoft.com/zh-cn/library/ms733766.aspx