基于支持向量机的动调陀螺仪寿命预测方法研究
|
摘要
随着我国航天技术的发展,航天器越来越向着高可靠、长寿命、高有效性方向发展。作为这些航天器中必不可少的姿态控制执行机构和姿态测量部件——飞轮、陀螺仪等旋转机电部件,由于其成本高、批量小等特点,如何评估这些产品及其部件的可靠性寿命成为一个迫切需要解决的难题。动调陀螺仪作为一种被广泛应用于航天器导航与制导系统中的中高精度陀螺仪,对其进行可靠性寿命预测方法研究具有十分重要的现实意义和理论研究价值。
本文根据学校实验室现有的试验条件,在只有单个动调陀螺仪试验子样情况下,给出了一个在极小子样条件下的动调陀螺仪寿命试验和寿命预测方法研究思路、方案,即以支持向量机作为理论基础,采用参数外推法进行动调陀螺仪寿命评估与预测方法研究。通过对动调陀螺仪在极小子样条件下进行1:1完全寿命试验,测试与分析影响动调陀螺仪寿命的性能参数,研究并提取能够表征陀螺可靠性和寿命的关键因子,通过对其的分析与处理建立寿命预测模型,进而对单个动调陀螺仪的寿命进行评估和预测研究。本文主要完成了如下几方面的工作:
第一,结合动调陀螺仪的结构特点及试验条件,采用一种新的寿命评估思路(即性能参数外推法)对陀螺仪寿命进行评估与预测研究,并给出了整个动调陀螺仪寿命预测研究方案。同时,根据此方案及陀螺仪自身的特点,确定陀螺振动、漂移、工作和环境温度、陀螺供电电压、功率等作为监测的性能参数,通过相应的测试电路、软件及界面等设计,建立动调陀螺仪寿命测试系统,完成其状态监测与数据采集。
第二,由于测试系统及动调陀螺仪自身的结构温度特性,文中结合小波分析优越的多尺度(多分辨率)分析特性,提出了一种基于小波变换和支持向量机的温度建模与补偿模型,对陀螺各参数的长期测试数据进行温度建模补偿与预处理。另外,根据陀螺仪自身的性能状态变化,采用基于支持向量机的启发式搜索策略,以陀螺参数特征集的交叉验证错误率为评价指标进行自学习识别与提取分析,最终选取陀螺振动作为关键特征因子对陀螺仪性能进行评估和预测研究。
第三,根据陀螺振动信号的非平稳特性,采用经验模态分解(EMD)的信号分析方法对陀螺振动进行分析。针对EMD分析方法自身的缺陷,文中提出了一种改进EMD方法的混合信号分析策略,并利用其对动调陀螺仪振动信号进行频域能量分析。同时,根据陀螺长期测试期间自身性能的变化,采用基于支持向量机的权重贡献分配方法对陀螺频域能量特征进行自学习提取,并以此建立反映动调陀螺仪性能状态渐进变化趋势的陀螺振动频率能量。
第四,结合支持向量机预测及灰色数据生成操作的优势,提出了一种新的灰色支持向量机预测模型(即AGO-SVM预测模型),并利用其对实际陀螺振动能量数据进行建模预测比较研究。预测结果表明所提出的灰色支持向量机模型预测精度优于单一灰色预测和SVM预测方法,具有更好的建模预测性能。另外,根据提取的陀螺寿命指标——振动特征能量趋势,利用灰色支持向量机预测模型对动调陀螺仪进行寿命评估与预测研究。同时,通过对单个动调陀螺仪历史能量数据进行多次分段建模预测统计分析,从而建立多步预测误差模型,以此来评估动调陀螺仪寿命预测的误差及可信度。
本论文的研究工作可为我国航天等领域机电旋转部件的寿命预测研究工作提供参考与借鉴。
With the development of aerospace technology, aerospace products are evolving towards high reliability, long lifetime and great effectiveness. However, these necessary gesture controlling and measuring components, such as flywheel and gyroscope, are high cost and small batch, so how to estimate their reliability and lifetime has become an urgent challenge. As a result, this research on lifetime evaluation and forecasting of a high precision dynamically tuned gyroscope (DTG), which is widely applied in the navigation systems of these aerospace products, is significant.
Due to the limitation of the lab’s testing conditions, this thesis presents a kind of research scheme for single gyro lifetime testing and forecasting based on the factor that only one DTG can be employed. That is, this scheme adopts trend modeling and extrapolation to estimate and forecast the DTG lifetime. In this scheme, the 1:1 testing system of the single DTG is first built and executed to collect the testing data of different performance parameters, and then the key factor is studied and selected from the different performance parameters based on the corresponding analysis methods. Through the analysis and processing of the key factor, the corresponding DTG lifetime forecasting model based on SVM is built and investigated, which is employed to evaluate and forecast the DTG lifetime. The main research contents of the thesis are listed as follows:
Firstly, owing to the configuration characteristic of the DTG and the testing conditions, a new lifetime evaluation scheme, the trend forecasting and extrapolation, is given to evaluate and forecast the lifetime of the single DTG. According to the given scheme, the different performance parameters of the DTG, such as gyro vibration, gyro drift (X- and Y-axis), gyro temperature, environmental temperature, gyro volt and power, are selected as the testing parameters, and the related testing circuits are designed, respectively. By designing of system soft and interface with Visual C++, the corresponding DTG lifetime testing system is built to accomplish the state measurement and data collection.
Secondly, due to the superior ability of multi-resolution analysis, wavelet transform (WT) is introduced into the SVM, and thus a novel WT-SVM temperature compensation model is proposed and applied in the temperature modeling and compensation to reduce the influence of temperature variation on the long-term testing data of the different performance parameters. The corresponding modeling and compensating results indicate that the proposed WT-SVM model is feasible and effective. In addition, according to the change of long-term performance state of the DTG, the SVM-based recursive feature elimination strategy and the evaluation criterion of cross-validating error rate are adopted to conduct the self-learning identification and gyro feature selection. Consequently, gyro vibration is selected as the key performance parameter for the DTG’s lifetime evaluating and forecasting investigation.
Thirdly, according to the nonlinear and non-stationary characteristic of the gyro vibration, empirical mode decomposition (EMD) method is employed to analyze the gyro vibration signal. Aiming at the problems of intrinsic mode function (IMF) criterion in single EMD method, neural network (NN) prediction model and wavelet packet transform (WPT) technology are introduced into the EMD method, and an improved hybrid EMD-based analysis strategy is thus proposed and applied in the frequency-domain energy analysis of the gyro vibration. Simultaneously, owing to the change of long-term performance state of the DTG, the distribution approach of weight contribution based on SVM is employed to extract the frequency-domain energy features of the DTG, with which the energy trend of the gyro vibration denoting the gradual performance tendency of the DTG is built.
Fourthly, combining the superiority of SVM forecasting and grey accumulated generating operation (AGO), a kind of new grey support vector machine (i.e. AGO-SVM model) forecasting model is proposed and applied in the forecasting analysis of gyro vibration energy. The modeling and forecasting results of the real energy data of the DTG show that the proposed strategy of the combination between AGO and SVM is effective and superior to the traditional grey model and single SVM method. In addition, according to the extracted gyro lifetime index (the vibration energy trend), the grey SVM model is exploited to forecast and analyze the residual lifetime of the DTG. Moreover, based on the statistical analysis of multiple subsection modeling and forecasting of the single DTG’s energy data, the multi-step forecasting error model of the grey SVM model is built, which can be used to estimate the forecasting error and reliability of the DTG lifetime.
In summary, the research of the thesis may be used as reference for lifetime forecasting research of other electromechanical rotating components in our aerospace or other fields.
本文根据学校实验室现有的试验条件,在只有单个动调陀螺仪试验子样情况下,给出了一个在极小子样条件下的动调陀螺仪寿命试验和寿命预测方法研究思路、方案,即以支持向量机作为理论基础,采用参数外推法进行动调陀螺仪寿命评估与预测方法研究。通过对动调陀螺仪在极小子样条件下进行1:1完全寿命试验,测试与分析影响动调陀螺仪寿命的性能参数,研究并提取能够表征陀螺可靠性和寿命的关键因子,通过对其的分析与处理建立寿命预测模型,进而对单个动调陀螺仪的寿命进行评估和预测研究。本文主要完成了如下几方面的工作:
第一,结合动调陀螺仪的结构特点及试验条件,采用一种新的寿命评估思路(即性能参数外推法)对陀螺仪寿命进行评估与预测研究,并给出了整个动调陀螺仪寿命预测研究方案。同时,根据此方案及陀螺仪自身的特点,确定陀螺振动、漂移、工作和环境温度、陀螺供电电压、功率等作为监测的性能参数,通过相应的测试电路、软件及界面等设计,建立动调陀螺仪寿命测试系统,完成其状态监测与数据采集。
第二,由于测试系统及动调陀螺仪自身的结构温度特性,文中结合小波分析优越的多尺度(多分辨率)分析特性,提出了一种基于小波变换和支持向量机的温度建模与补偿模型,对陀螺各参数的长期测试数据进行温度建模补偿与预处理。另外,根据陀螺仪自身的性能状态变化,采用基于支持向量机的启发式搜索策略,以陀螺参数特征集的交叉验证错误率为评价指标进行自学习识别与提取分析,最终选取陀螺振动作为关键特征因子对陀螺仪性能进行评估和预测研究。
第三,根据陀螺振动信号的非平稳特性,采用经验模态分解(EMD)的信号分析方法对陀螺振动进行分析。针对EMD分析方法自身的缺陷,文中提出了一种改进EMD方法的混合信号分析策略,并利用其对动调陀螺仪振动信号进行频域能量分析。同时,根据陀螺长期测试期间自身性能的变化,采用基于支持向量机的权重贡献分配方法对陀螺频域能量特征进行自学习提取,并以此建立反映动调陀螺仪性能状态渐进变化趋势的陀螺振动频率能量。
第四,结合支持向量机预测及灰色数据生成操作的优势,提出了一种新的灰色支持向量机预测模型(即AGO-SVM预测模型),并利用其对实际陀螺振动能量数据进行建模预测比较研究。预测结果表明所提出的灰色支持向量机模型预测精度优于单一灰色预测和SVM预测方法,具有更好的建模预测性能。另外,根据提取的陀螺寿命指标——振动特征能量趋势,利用灰色支持向量机预测模型对动调陀螺仪进行寿命评估与预测研究。同时,通过对单个动调陀螺仪历史能量数据进行多次分段建模预测统计分析,从而建立多步预测误差模型,以此来评估动调陀螺仪寿命预测的误差及可信度。
本论文的研究工作可为我国航天等领域机电旋转部件的寿命预测研究工作提供参考与借鉴。
With the development of aerospace technology, aerospace products are evolving towards high reliability, long lifetime and great effectiveness. However, these necessary gesture controlling and measuring components, such as flywheel and gyroscope, are high cost and small batch, so how to estimate their reliability and lifetime has become an urgent challenge. As a result, this research on lifetime evaluation and forecasting of a high precision dynamically tuned gyroscope (DTG), which is widely applied in the navigation systems of these aerospace products, is significant.
Due to the limitation of the lab’s testing conditions, this thesis presents a kind of research scheme for single gyro lifetime testing and forecasting based on the factor that only one DTG can be employed. That is, this scheme adopts trend modeling and extrapolation to estimate and forecast the DTG lifetime. In this scheme, the 1:1 testing system of the single DTG is first built and executed to collect the testing data of different performance parameters, and then the key factor is studied and selected from the different performance parameters based on the corresponding analysis methods. Through the analysis and processing of the key factor, the corresponding DTG lifetime forecasting model based on SVM is built and investigated, which is employed to evaluate and forecast the DTG lifetime. The main research contents of the thesis are listed as follows:
Firstly, owing to the configuration characteristic of the DTG and the testing conditions, a new lifetime evaluation scheme, the trend forecasting and extrapolation, is given to evaluate and forecast the lifetime of the single DTG. According to the given scheme, the different performance parameters of the DTG, such as gyro vibration, gyro drift (X- and Y-axis), gyro temperature, environmental temperature, gyro volt and power, are selected as the testing parameters, and the related testing circuits are designed, respectively. By designing of system soft and interface with Visual C++, the corresponding DTG lifetime testing system is built to accomplish the state measurement and data collection.
Secondly, due to the superior ability of multi-resolution analysis, wavelet transform (WT) is introduced into the SVM, and thus a novel WT-SVM temperature compensation model is proposed and applied in the temperature modeling and compensation to reduce the influence of temperature variation on the long-term testing data of the different performance parameters. The corresponding modeling and compensating results indicate that the proposed WT-SVM model is feasible and effective. In addition, according to the change of long-term performance state of the DTG, the SVM-based recursive feature elimination strategy and the evaluation criterion of cross-validating error rate are adopted to conduct the self-learning identification and gyro feature selection. Consequently, gyro vibration is selected as the key performance parameter for the DTG’s lifetime evaluating and forecasting investigation.
Thirdly, according to the nonlinear and non-stationary characteristic of the gyro vibration, empirical mode decomposition (EMD) method is employed to analyze the gyro vibration signal. Aiming at the problems of intrinsic mode function (IMF) criterion in single EMD method, neural network (NN) prediction model and wavelet packet transform (WPT) technology are introduced into the EMD method, and an improved hybrid EMD-based analysis strategy is thus proposed and applied in the frequency-domain energy analysis of the gyro vibration. Simultaneously, owing to the change of long-term performance state of the DTG, the distribution approach of weight contribution based on SVM is employed to extract the frequency-domain energy features of the DTG, with which the energy trend of the gyro vibration denoting the gradual performance tendency of the DTG is built.
Fourthly, combining the superiority of SVM forecasting and grey accumulated generating operation (AGO), a kind of new grey support vector machine (i.e. AGO-SVM model) forecasting model is proposed and applied in the forecasting analysis of gyro vibration energy. The modeling and forecasting results of the real energy data of the DTG show that the proposed strategy of the combination between AGO and SVM is effective and superior to the traditional grey model and single SVM method. In addition, according to the extracted gyro lifetime index (the vibration energy trend), the grey SVM model is exploited to forecast and analyze the residual lifetime of the DTG. Moreover, based on the statistical analysis of multiple subsection modeling and forecasting of the single DTG’s energy data, the multi-step forecasting error model of the grey SVM model is built, which can be used to estimate the forecasting error and reliability of the DTG lifetime.
In summary, the research of the thesis may be used as reference for lifetime forecasting research of other electromechanical rotating components in our aerospace or other fields.
引文
[1]沈建波.电子元器件可靠性技术在发展航天中的重要作用.导弹与航天运载技术, 1999, 13(5): 55-62.
[2]武晓燕.航天电子系统可靠性预计工程实践浅说.航空精密制造技术, 1994(3): 38-41.
[3]金恂叔.论航天器的环境试验和可靠性.中国空间科学技术, 1997(5): 33-40.
[4]李晓钢.环境工程技术对航空航天产品可靠性的研究.北京航空航天大学学报, 1995(4): 106-111.
[5]金恂叔.航天器研制中的环境是演及其发展趋势(上).环境技术, 2001(5): 16-18.
[6]金恂叔.航天器研制中的环境是演及其发展趋势(下).环境技术, 2001(6): 27-34.
[7]冯培德.可靠性工程技术在航空惯性导航系统研制中的应用.中国惯性技术学报, 1996, 4(4): 56-65.
[8]张士峰. Bayes可靠性评估方法述评.飞行器测控学报, 2000, 19(2): 28-34.
[9] William Denson. The history of reliability prediciton. IEEE Transactions on Reliability, 1998, 47(3): 321-328.
[10]韩庆田.可靠性预计及其发展趋向.电子标准化与质量, 2001(5): 7-9.
[11] J. S. Bendat and A. G. Piersol.相关分析和谱分析的工程应用,凌福根译.北京:国防工业出版社, 1983.
[12]胡广书.数字信号处理,北京:清华大学出版社, 1988.
[13] Xing-Qi Jiang and Genshiro Kitagawa. Time varying coefficient vector AR modeling of nonstationary covariance time series. Signal Processing, 1993, 33(3): 315-331.
[14] Y. Zheng, D. B. H. Tay and Z. Lin. Modeling general distributed nonstationary process and identifying time-varying autoregressive system by wavelets: Theory and application. Signal Processing, 2001, 81(9): 1823-1848.
[15] Edgar F. Velez and Richard G. Absher. Parametric modeling of the Wigner half-kernel and its application to spectral estimate. Signal Processing, 1992, 26(2): 161-175.
[16] F. Shen, M. Zheng, D. Feng Shi, et al. Using the cross-correlation technique to extract modal parameters on response-only data. Journal of Sound and Vibration, 2003, 259(5): 1163-1179.
[17] R. J. Martin. Autoregression and irregular sampling: spectral estimation. Signal Processing, 1999, 77(2): 139-157.
[18] G. Meltzer and Nguyen Phong Dien. Fault diagnosis in gears operating under non-stationary rotational speed using polar wavelet amplitude maps. Mechanical Systems and Signal Processing, 2004, 18(5): 985-992.
[19] Wilson Q. Wang, M. Farid Golnaraghi and Fathy Ismail. Prognosis of machine health condition using neuro-fuzzy systems. Mechanical Systems and Signal Processing, 2004, 18(4): 813-831.
[20] Z. K. Peng and F. L. Chu. Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography. Mechanical Systems and Signal Processing, 2004, 18(2): 199-221.
[21] Z. Peng, F. Chu and Y. He. Vibration signal analysis and feature extraction based on reassigned wavelet scalogram. Journal of Sound and Vibration, 2002, 253(5): 1087-1100.
[22] N. E. Huang, Z. Shen, S. R. Long, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, 1998, 454(1971): 903-995.
[23]钟佑明.一种振动信号新变换法的研究.振动工程学报, 2002, 15(2): 233-238.
[24] Norden E. Huang, Zheng Shen and Steven R. Long. A new view of nonlinear water waves: The Hilbert spectrum. Annual Review of Fluid Mechanics, 1999, 31: 417-457.
[25] H. T. Vicent. Damage detection using empirical mode decomposition method and a comparison with wavelet analysis. in: Proceedings of the Second International Workshop on Structural Health Monitoring, Stanford, 1998: 891-900.
[26] Paul A. Hwang, Norden E. Huang and David W. Wang. A note on analyzing nonlinear and nonstationary ocean wave data. Applied Ocean Research, 2003, 25(4): 187-193.
[27] A. D. Veltcheva and C. Guedes Soares. Identification of the components of wave spectra by the Hilbert Huang transform method. Applied Ocean Research, 2004, 26(1-2): 1-12.
[28] D. J. Yu, J. S. Cheng and Y. Yang. Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings. Mechanical Systems and Signal Processing, 2005, 19(2): 259-270.
[29] G. H. Gai. The processing of rotor startup signals based on empirical mode decomposition. Mechanical Systems and Signal Processing, 2006, 20(1): 222-235.
[30]宋世斌.一种威布尔寿命分布模型.电子产品可靠性与环境试验, 2001(1): 7-9.
[31] Amos E. Gera. Modified exponentiated-Weibull distribution for life-time modeling. Philadelphia, PA, USA, 1997149-152.
[32] G. Pulcini. An exponential reliability-growth model in multicopy testing program. Ieee Transactions on Reliability, 2001, 50(4): 365-373.
[33] Kevin Fitzgibbon, Ron Barker, Tige Clayton, et al. A failure-forecast method based on Weibull and statistical-pattern analysis. Seattle, WA, 2002516-521.
[34] Lev M. Klyatis, Oleg I. Teskin and James W. Fulton. Multi-variate Weibull model for predicting system-reliability, from testing results of the components. Proceedings of the Annual Reliability and Maintainability Symposium, 2000: 144-149.
[35] John S. Usher. Weibull component reliability-prediction in the presence of masked data. IEEE Transactions on Reliability, 1996, 45(2): 229-232.
[36] Alexander Filippenko Apex. Vibration analysis for predictive maintenance of rotating machines, Patent No.: US 6370957B1, Apr. 16, 2002.
[37]布洛克威尔(美国),田铮译.时间序列的理论与方法(第二版),高等教育出版社, 2001.
[38]顾岚.时间序列分析,中国统计出版社, 1994.
[39]徐峰. AR模型应用于振动信号趋势预测的研究.清华大学学报(自然科学版), 1999, 39(4): 57-59.
[40]阎长俊.用AR模型预测机械故障的方法.沈阳建筑工程学院学报, 1995, 11(2): 138-141.
[41] Joseph E. Killpatrick. Laser gyro life prediction, Patent No.: US005719675A, Feb. 17, 1998.
[42]周长银.非线性Bayes动态模型预测的数值算法.经济数学, 2001(3): 58-61.
[43]周源泉.极值分布参数、可靠寿命、可靠性的Bayes精确区间估计.导弹与航天运载技术, 1997(4): 55-62.
[44]唐雪梅.具有不确定数据的指数可靠性增长的Bayes方法.航天控制, 1997(1): 64-68.
[45]张孝令.贝叶斯动态模型及其预测,济南:山东科学技术出版社, 1992.
[46] J. Rene van Dorp, Thomas A. Mazzuchi, Gordon E. Fornell, et al. Bayes approach to step-stress accelerated life testing. IEEE Transactions on Reliability, 1996, 45(3): 491-498.
[47] Wayne Nelson. ACCELERATED LIFE TESTING EM DASH STEP-STRESS MODELS AND DATA ANALYSES. IEEE Transactions on Reliability, 1980, R-29(2): 103-108.
[48] Robert Miller and Wayne Nelson. OPTIMUM SIMPLE STEP-STRESS PLANS FOR ACCELERATED LIFE TESTING. IEEE Transactions on Reliability, 1983, R-32(1):59-65.
[49] D. S. Bai and Y. R. Chun. Optimum simple step-stress accelerated life-tests with competing causes of failure. IEEE Transactions on Reliability, 1991, 40(5): 622-627.
[50] J. Rene Van Dorp, Thomas A. Mazzuchi and Jorge E. Garciduenas. A comparison of accelerated life testing designs within A single Bayesian inferential framework. Newport Beach, CA, United States, 2006208-214.
[51] William J. Zimmer and John J. Deely. Bayes ranking of survival distributions using accelerated or correlated data. IEEE Transactions on Reliability, 1996, 45(3): 499-504.
[52] David Robinson and Duane Dietrich. Nonparametric-Bayes reliability-growth model. IEEE Transactions on Reliability, 1989, 38(5): 591-598.
[53] E. P. Virene. Reliability growth and its upper limit. in: Proceedings of Annual Reliability and Maintainability Symposium, 1968, 265-270.
[54] D. Robinson and D. Dietrich. A New Nonparametric Growth Model. IEEE Transactions on Reliability, 1987, 36: 411-418.
[55] C. A. Clarotti and F. Spizzichino. Bayes predictive approach in reliability theory. IEEE Transactions on Reliability, 1989, 38(3): 379-382.
[56] Aric Dumai and Avi Winkler. Relaiblity prediction model for gyroscope. in: Proceedings Annual Reliability and Maintainability Symposium, 1990, 5-9.
[57]邓聚龙.灰色控制系统,华中工学院出版社, 1985.
[58]邓聚龙.灰色系统理论教程,华中理工大学出版社, 1990.
[59]任玉笋.灰色系统理论、数据预处理及其应用.上海交通大学学报, 2001, 35(2): 268-271.
[60]樊春玲.长寿命动调陀螺仪的寿命预测方法研究,上海交通大学,Ph.D. thesis, 2004.
[61]杨江天.灰色模型在机械故障预测中的应用.机械强度, 2001, 23(3): 277-279.
[62] Jacek M. Zurada. Introduction to Artificial Neural Systems, New York, Los Angeles, San Francisco: West Publishing Company, 1992.
[63]陈增强.基于神经网络的非线性预测控制综述.控制工程, 2002, 9(4): 7-11.
[64] A. G. Parlos, O. T. Rais and A. F. Atiya. Multi-step-ahead prediction using dynamic recurrent neural networks. Neural Networks, 2000, 13(7): 765-786.
[65] Hong-Te Su, Thomas J. McAvoy and Paul Werbos. Long-term predictions of chemical processes using recurrent neural networks. A parallel training approach. Industrial & Engineering Chemistry Research, 1992, 31(5): 1338-1352.
[66] Guy-Michel Cloarec and John Ringwood. Incorporation of statistical methods inmulti-step neural network prediction models. Anchorage, AK, USA, 1998, 3, pp. 2513-2518.
[67] A. G. Parlos, K. T. Chong and A. F. Atiya. Application of the neural multiplayer perception in modeling complex process dynamics. IEEE Transactions on Neural Netoworks: Sepcial Issue on Recurrent Dynamic Neural networks, 1994, 5(2): 255-266.
[68]潘紫微.基于神经网络的多特征和多步轴承寿命预测方法.机械科学与技术, 1999, 18(4): 584-586.
[69]曾令可.人工神经网络及其在硅酸铝纤维寿命预测中的应用.中国陶瓷工业, 2000, 7(3): 28-31.
[70] V. N. Vapnik. The nature of statistical learning theory, New York: Springer, 1995.
[71] A. J. Smola and B. Scholkopf. A tutorial on support vector regression. Statistics and Computing, 2004, 14(3): 199-222.
[72] Steve R. Gunn. Support vector machines for classification and regression, Technical Report, University of Southampton, 1998.
[73] Haisheng Li and Xuefeng Zhu. Application of support vector machine method in prediction of kappa number of kraft pulping process. Hangzhou, China, 2004, 4, pp. 3325-3330.
[74] Sayan Mukherjee, Edgar Osuna and Federico Girosi. Nonlinear prediction of chaotic time series using support vector machines. Amelia Island, FL, USA, 1997511-520.
[75] F. E. H. Tay and L. J. Cao. Application of support vector machines in financial time series forecasting. Omega-International Journal of Management Science, 2001, 29(4): 309-317.
[76] J. I. Yeh and L. S. Mao. Prediction of membrane proteins in Mycobacterium tuberculosis using a Support Vector Machine algorithm. Journal of Computational Biology, 2006, 13(1): 126-129.
[77] D. P. Cui and D. Curry. Prediction in marketing using the support vector machine. Marketing Science, 2005, 24(4): 595-615.
[78]胡金海.基于支持向量机方法的发动机性能趋势预测.推进技术, 2005, 26(3): 260-264.
[79]李凌均.基于支持向量机的机械设备状态趋势预测研究.西安交通大学学报, 2004, 38(3): 230-234.
[80] F. E. H. Tay and L. J. Cao. Modified support vector machines in financial time series forecasting. Neurocomputing, 2002, 48: 847-861.
[81] L. J. Cao and F. E. H. Tay. Support vector machine with adaptive parameters in financial time series forecasting. Ieee Transactions on Neural Networks, 2003, 14(6): 1506-1518.
[1] V. N. Vapnik. The nature of statistical learning theory, New York: Springer-Verlag, 1995.
[2]安金龙.支持向量机若干问题的研究,天津大学, Ph.D. thesis, 2004.
[3] S.R. Gunn. Support vector machines for classification and regression. Technical Report ISIS-1-98, University of Southampton, 1998.
[4]范昕炜.支持向量机算法的研究及其应用,浙江大学, Ph.D. thesis, 2003.
[5] V. N. Vapnik. An overview of statistical learning theory. Ieee Transactions on Neural Networks, 1999, 10(5): 988-999.
[6] Y. S. Abu-Mostafa. The Vapnik-Chervonenkis dimension: information versus complexity in learning. Neural Computation, 1989, 1(3): 312-317.
[7] P. Koiran and E. D. Sontag. Vapnik-Chervonenkis dimension of recurrent neural networks. Discrete Applied Mathematics, 1998, 86(1): 63-79.
[8] M. Schmitt. On using the Poincare polynomial for calculating the VC dimension of neural networks. Neural Networks, 2001, 14(10): 1465-1465.
[9] Vladimir Vapnik, Esther Levin and Yann Le Cun. Measuring the VC-dimension of a learning machine. Neural Computation, 1994, 6(5): 851.
[10] C. J. C. Burges. A tutorial on Support Vector Machines for pattern recognition. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167.
[11] J. Mercer. Functions of positive and negative type and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society of London, Series A, 1909, 209: 415-446.
[12] A. J. Smola and B. Scholkopf. A tutorial on support vector regression. Statistics and Computing, 2004, 14(3): 199-222.
[1]周百令.动力调谐陀螺仪设计与制造,南京:东南大学出版社, 2002.
[2] PC-Labcard. PCL-818L User's Manual, Advantech corporation, 1995.
[3]田自耘.陀螺仪漂移测试基础,北京:国防工业出版社, 1998.
[4] Gerard N. Stenbakken and Amos Dolev. High-accuracy sampling wattmeter. IEEE Transactions on Instrumentation and Measurement, 1992, 41(6): 974-978.
[5] Gerard N. Stenbakken. A wideband sampling wattmeter. IEEE Transactions on Power Apparatus and System, 1984, 103(10): 2919-2926.
[6] David J. Kruglinski. Visual C++技术内幕(第四版),北京:清华大学出版社, 1999.
[7]侯俊杰.深入浅出MFC(第二版),武汉:华中科技大学出版社, 2001.
[8] Jeffrey Richter. Windows核心编程,北京:机械工业出版社, 2000.
[9]钱能. C++程序设计教程,北京:清华大学出版社, 1999.
[1]余勇,动力调谐陀螺温度漂移的建模研究,东南大学, Ph.D. thesis, 2000.
[2] J. L. Deng. Introduction to grey system theory. The Journal of Grey System, 1989, 1(1): 1-24.
[3] Zhijie Chen, Weizhen Chen, Qile Chen, et al. Trend relational analysis and grey-fuzzy clustering method. Bethesda, MD, United States, 2006, pp. 234-245.
[4] M. Lu and K. Wevers. Application of grey relational analysis for evaluating road traffic safety measures: Advanced driver assistance systems against infrastructure redesign. IET Intelligent Transport Systems, 2007, 1(1): 3-14.
[5] K. C. Chang and M. F. Yeh. Grey relational analysis based approach for data clustering. IEE Proceedings: Vision, Image and Signal Processing, 2005, 152(2): 165-172.
[6] G. P. Xu, W. F. Tian, L. Qian, et al. A novel conflict reassignment method based on grey relational analysis (GRA). Pattern Recognition Letters, 2007, 28(15): 2080-2087.
[7] Anton Plotkin and Eugene Paperno. Compensation of temperature-drift errors with no additional hardware. IEEE Instrumentation and Measurement Magazine, 2007, 10(5): 20-25.
[8]诸一江.陀螺仪温度模型建模方法介绍.中国惯性技术学报, 1995, 3(1): 32-37.
[9]郝玉红.温度变化引起的静压液浮陀螺漂移的补偿研究.惯导与仪表, 1996, 2: 51-56.
[10]王淑娟.加速度计温度模型辨识.中国惯性技术学报, 1997, 5(1): 31-36.
[11] Qing Dong Yang, Christophe Van den Bergh, Paul Vanherck, et al. Linear regression and neural net models applied to thermal error compensation in machine tools. European Journal of Mechanical and Environmental Engineering, 1999, 44(3): 146-152.
[12] Jiun-Hong Lai and Chin-Teng Lin. Application of neural fuzzy network to pyrometer correction and temperature control in rapid thermal processing. IEEE Transactions on Fuzzy Systems, 1999, 7(2): 160-175.
[13] Christopher D. Mize and John C. Ziegert. Neural network thermal error compensation of a machining center. Precision Engineering, 2000, 24(4): 338-346.
[14] J. M. Dias Pereira, Octavian Postolache, P. M. B. Girao, et al. Minimizing temperature drift errors of conditioning circuits using artificial neural networks. IEEE Transactions on Instrumentation and Measurement, 2000, 49(5): 1122-1127.
[15] S. Yang, J. Yuan and J. Ni. Improvement of thermal error modeling and compensation onmachine tools by CMAC neural network. International Journal of Machine Tools & Manufacture, 1996, 36(4): 527-537.
[16] H. Yang and J. Ni. Dynamic neural network modeling for nonlinear, nonstationary machine tool thermally induced error. International Journal of Machine Tools & Manufacture, 2005, 45(4-5): 455-465.
[17] R. Ramesh, M. A. Mannan and A. N. Poo. Thermal error measurement and modelling in machine tools.: Part I. Influence of varying operating conditions. International Journal of Machine Tools and Manufacture, 2003, 43(4): 391-404.
[18] R. Ramesh, M. A. Mannan, A. N. Poo, et al. Thermal error measurement and modelling in machine tools. Part II. Hybrid Bayesian Network - Support vector machine model. International Journal of Machine Tools and Manufacture, 2003, 43(4): 405-419.
[19] G. P. Xu, W. F. Tian, Z. H. Jin, et al. Temperature drift modelling and compensation for a dynamically tuned gyroscope by combining WT and SVM method. Measurement Science & Technology, 2007, 18(5): 1425-1432.
[20] David L. Donoho. De-noising by soft-thresholding. IEEE Transactions on Information Theory, 1995, 41(3): 613-627.
[21] Chau-Jern Cheng and Li-Chien Lin. Optical wavelet subband filtering for joint transform correlation. Optical Engineering, 2005, 44(10): 105001.
[22] James E. Fowler. The redundant discrete wavelet transform and additive noise. IEEE Signal Processing Letters, 2005, 12(9): 629-632.
[23] S. Mallat. A theory for multi-resolution decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Machine Intell., 1989, 11(7): 674-693.
[24] I. Daubechies. The wavelet transform time-frequency localization and signal analysis. IEEE Trans. on Information Theory, 1990, 36(5): 961-1005.
[25] D. Sko?aj, Robust subspace approaches to visual learning and recognition. 2003: Computer and Information Science, University of Ljubljana, Ph.D. thesis.
[26] Chanchal Chatterjee, Zhengjiu Kang and Vwani P. Roychowdhury. Algorithms for accelerated convergence of adaptive PCA. IEEE Transactions on Neural Networks, 2000, 11(2): 338-355.
[27] Vo Dinh Minh Nhat and Sung Young Lee. Two-dimensional weighted PCA algorithm for face recognition. Espoo, Finland, 2005219-223.
[28] Masaaki Shikada, Noboru Muramatu, Takashi Kusaka, et al. Extraction of landslide areas using satellite remote sensing and GIS technology. in: International Geoscience andRemote Sensing Symposium (IGARSS), Italy, 1995, 1, pp. 377-379.
[29] N. Louw and S. J. Steel. Variable selection in kernel Fisher discriminant analysis by means of recursive feature elimination. Computational Statistics and Data Analysis, 2006, 51(3): 2043-2055.
[30] Ha-Nam Nguyen and Syng-Yup Ohn. DRFE: Dynamic recursive feature elimination for gene identification based on random forest. Lecture Notes in Computer Science, 2006, 4234 NCS - III: 1-10.
[31]薛毅,支持向量机与数学规划,北京工业大学, Ph.D. thesis, 2003.
[32] F. E. Sistler. Grading agriculture products with machine visio. IEEE International Workshop on Intelligent Robots and System, 1990: 255-261.
[33]任东,基于支持向量机的植物病害识别研究,吉林大学, Ph.D. thesis, 2007.
[34] A. F. Apex, S. B. Wake Forest and A. N. Morrisville, Vibration analysis for predictive maintenance of rotating machines. 2002: United States Patent: US6370957B1.
[35] Runqing Huang, Lifeng Xi, Xinglin Li, et al. 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.
[36] G. H. Gai. The processing of rotor startup signals based on empirical mode decomposition. Mechanical Systems and Signal Processing, 2006, 20(1): 222-235.
[37] N. Gebraeel, M. Lawley, R. Liu, et al. Residual life, predictions from vibration-based degradation signals: A neural network approach. Ieee Transactions on Industrial Electronics, 2004, 51(3): 694-700.
[1] D. Gabor. Theory of communication. J. IEE, 1946, 93(26): 429-457.
[2] G. Meltzer and Y. Y. Ivanov. Fault detection in gear drives with non-stationary rotational speed-part I: The time-frequency approach. Mechanical Systems and Signal Processing, 2003, 17(5): 1033-1047.
[3] S. A. Neild, P. D. McFadden and M. S. Williams. A review of time-frequency methods for structural vibration analysis. Engineering Structures, 2003, 25(6): 713-728.
[4] J. Zou and J. Chen. A comparative study on time-frequency feature of cracked rotor by Wigner-Ville distribution and wavelet transform. Journal of Sound and Vibration, 2004, 276(1-2): 1-11.
[5] F. L. Di Scalea and J. McNamara. Measuring high-frequency wave propagation in railroad tracks by joint time-frequency analysis. Journal of Sound and Vibration, 2004, 273(3): 637-651.
[6] J. Kim, D. E. Welcome, R. G. Dong, et al. Time-frequency characterization of hand-transmitted, impulsive vibrations using analytic wavelet transform. Journal of Sound and Vibration, 2007, 308(1-2): 98-111.
[7] N. E. Huang, Z. Shen, S. R. Long, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, 1998, 454(1971): 903-995.
[8] N. E. Huang, M. L. C. Wu, S. R. Long, et al. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proceedings of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, 2003, 459(2037): 2317-2345.
[9] H. T. Vicent. Damage detection using empirical mode decomposition method and a comparison with wavelet analysis. in: Proceedings of the Second International Workshop on Structural Health Monitoring, Stanford, 199891-900.
[10] G. H. Gai. The processing of rotor startup signals based on empirical mode decomposition. Mechanical Systems and Signal Processing, 2006, 20(1): 222-235.
[11] D. J. Yu, J. S. Cheng and Y. Yang. Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings. Mechanical Systems and Signal Processing, 2005, 19(2): 259-270.
[12]熊卫华,经验模态分解方法及其在变压器状态监测中的应用研究,浙江大学,Ph.D. thesis, 2006.
[13]赵治栋.基于Hilbert-Huang Transform的心音信号谱分析.传感技术学报, 2005, 18(1): 18-22.
[14]杨宇,基于EMD和支持向量机的旋转机械故障诊断方法研究,湖南大学,Ph.D. thesis, 2005.
[15] G. Xu, W. Tian and L. Qian. EMD- and SVM-based temperature drift modeling and compensation for a dynamically tuned gyroscope (DTG). Mechanical Systems and Signal Processing, 2007, 21(8): 3182-3188.
[16] B. Xuan, Q. W. Xie and S. L. Peng. EMD sifting based on bandwidth. Ieee Signal Processing Letters, 2007, 14(8): 537-540.
[17] Leon Cohen. Time-Frequency Analysis: Theory and Application, New York: Prentice Hall, 1995.
[18] S. Olhede and A. T. Walden. A generalized demodulation approach to time-frequency projections for multicomponent signals. in: Proceedings of the Royal Society a-Mathematical Physical and Engineering Sciences, 2005, 461, pp. 2159-2179.
[19] N. E. Huang, Computer implicated empirical mode decomposition method, apparatus, and article of manufacture. 1996: U.S. Patent Pending.
[20]陈忠. EMD信号分析方法边缘效应的分析.数据采集与处理, 2003, 18(1): 114-118.
[21] D. J. Huang, J. P. Zhao and J. L. Su. Practical implementation of Hilbert-Huang Transform algorithm. Acta Oceanologica Sinica, 2003, 22(1): 1-14.
[22]张郁山.利用自回归模型处理EMD方法中的边界问题.自然科学进展, 2003, 13(10): 1054-1059.
[23] Y. J. Deng, W. Wang, C. C. Qian, et al. Boundary-processing-technique in EMD method and Hilbert transform. Chinese Science Bulletin, 2001, 46(11): 954-961.
[24] M. J. D. Powell, Radial basis functions for multivariable interpolation: Review IMA conf. on algorithms for the approximation of functions data, RMCS Shrivanham, 1985.
[25] D. S. Broomhead and D. Lowe. Multivariate functional interpolation and adaptive networks Complex Systems, 1988, 2: 321-355.
[26] S. Chen, C. F. N. Cowan and P. M. Grant. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks, 1991, 2(2): 302-309.
[27] E. S. Chng, S. Chen and B. Mulgrew. Gradient radial basis function networks fornonlinear and nonstationary time series prediction. IEEE Transactions on Neural Networks, 1996, 7(1): 190-194.
[28] M. V. Wickerhauser. Adapted wavelet analysis from theory to software, New York: IEEE Press, 1994.
[29] A. F. Apex, S. B. Wake Forest and A. N. Morrisville, Vibration analysis for predictive maintenance of rotating machines. 2002: United States Patent: US6370957B1.
[30] Y. Shao and K. Nezu. Prognosis of remaining bearing life using neural networks. Proceedings of the Institution of Mechanical Engineers Part I-Journal of Systems and Control Engineering, 2000, 214(13): 217-230.
[31]李书明,制造设备智能诊断与状态预测技术的研究,天津大学,Ph.D. thesis, 1998.
[32] R. Q. Huang, L. F. Xi, X. L. Li, et al. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mechanical Systems and Signal Processing, 2007, 21: 193-207.
[33] D. Ho and R. B. Rand. Optimization of bearing diagnostic techniques using simulated and actual bearing fault signal. Mechanical Systems and Signal Processing, 2000, 14(5): 763-788.
[34] M. J. Boek. Experiments in the application of neural networks to rotating machine fault diagnosis. in: Proceedings of IEEE International Joint Conference on Neural Networks, 1991, 1, pp. 769-774.
[35] N. Gebraeel, M. Lawley, R. Liu, et al. Residual life, predictions from vibration-based degradation signals: A neural network approach. Ieee Transactions on Industrial Electronics, 2004, 51(3): 694-700.
[36]侯军虎,基于多参数的风机状态监测与故障诊断的研究,华北电力大学,Ph.D. thesis, 2003.
[37] X. Zhang and W. H. Wong, Recursive sample classification and gene selection based on SVM: method and software description. 2001: Technical Report, Dept. of Biostatistics, Harvard School of Public Health.
[1] Hui-Kuo Chang, Hsing-Chia Kuo and Yen-Zen Wang. Novel grey model for diesel engine oil monitoring. Journal of Ship Research, 2006, 50(1): 31-37.
[2] T. H. M. El-Fouly, E. F. El-Saadany and M. M. A. Salama. Grey predictor for wind energy conversion systems output power prediction. IEEE Transactions on Power Systems, 2006, 21(3): 1450-1452.
[3] Xue-Liang Ping, Sheng-Lan Liu, Jin-Liang Li, et al. Application of grey prediction for predicting the measuring point for a CMM. International Journal of Advanced Manufacturing Technology, 2007, 32(3-4): 288-292.
[4] Tzu-Li Tien. The indirect measurement of tensile strength of material by the grey prediction model GMC(1, n). Measurement Science and Technology, 2005, 16(6): 1322-1328.
[5] Min Han, Jianhui Xi, Shiguo Xu, et al. Prediction of chaotic time series based on the recurrent predictor neural network. IEEE Transactions on Signal Processing, 2004, 52(12): 3409-3416.
[6] Runqing Huang, Lifeng Xi, Xinglin Li, et al. 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.
[7] Raymond S. T. Lee. iJADE stock advisor: An intelligent agent based stock prediction system using hybrid RBF recurrent network. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans., 2004, 34(3): 421-427.
[8] A. K. Singh, S. S. Panda, D. Chakraborty, et al. Predicting drill wear using an artificial neural network. International Journal of Advanced Manufacturing Technology, 2006, 28(5-6): 456-462.
[9] E. Varoglu and K. Hacioglu. Recurrent neural network speech predictor based on dynamical systems approach. IEE Proceedings: Vision, Image and Signal Processing, 2000, 147(2): 149-156.
[10] V. N. Vapnik. The nature of statistical learning theory, New York: Springer-Verlag, 1995.
[11] C. Nello and S. T. John.支持向量机导论,电子工业出版社, 2004.
[12] L. J. Cao and F. E. H. Tay. Support vector machine with adaptive parameters in financial time series forecasting. Ieee Transactions on Neural Networks, 2003, 14(6): 1506-1518.
[13] F. E. H. Tay and L. J. Cao. Application of support vector machines in financial time series forecasting. Omega-International Journal of Management Science, 2001, 29(4): 309-317.
[14] F. E. H. Tay and L. J. Cao. Modified support vector machines in financial time series forecasting. Neurocomputing, 2002, 48: 847-861.
[15] Ciwei Gao, Ettore Bompard, Roberto Napoli, et al. Price forecast in the competitive electricity market by support vector machine. Physica A: Statistical Mechanics and its Applications, 2007, 382(1): 98-113.
[16] Mohamed Mohandes. Support vector machines for short-term electrical load forecasting. International Journal of Energy Research, 2002, 26(4): 335-345.
[17] Ping-Feng Pai and Wei-Chiang Hong. Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Conversion and Management, 2005, 46(17): 2669-2688.
[18] Bo-Juen Chen, Ming-Wei Chang and Chih-Jen Lin. Load forecasting using support vector machines: A study on EUNITE Competition 2001. IEEE Transactions on Power Systems, 2004, 19(4): 1821-1830.
[19] G. P. Xu, W. F. Tian and Z. H. Jin. An AGO-SVM drift modelling method for a dynamically tuned gyroscope. Measurement Science & Technology, 2006, 17(1): 161-167.
[20] J. L. Deng. Introduction to grey system theory. The Journal of Grey System, 1989, 1(1): 1-24.
[21]邓聚龙.灰色理论基础,武汉:华中科技大学出版社, 2002.
[22] H. T. Lu, W. J. Kolarik and S. S. Lu. Real-time performance reliability prediction. Ieee Transactions on Reliability, 2001, 50(4): 353-357.
[23] S. Lu, H. Lu and W. J. Kolarik. Multivariate performance reliability prediction in real-time. Reliability Engineering & System Safety, 2001, 72(1): 39-45.
[24] C. Chatfield, A. B. Koehler, J. K. Ord, et al. A new look at models for exponential smoothing. Journal of the Royal Statistical Society Series D-the Statistician, 2001, 50: 147-159.
[25] F. L. Greitzer and T. A. Ferryman. Predicting remaining life of mechanical systems. in: Intelligent Ship Symposium IV, 2001.
[2]武晓燕.航天电子系统可靠性预计工程实践浅说.航空精密制造技术, 1994(3): 38-41.
[3]金恂叔.论航天器的环境试验和可靠性.中国空间科学技术, 1997(5): 33-40.
[4]李晓钢.环境工程技术对航空航天产品可靠性的研究.北京航空航天大学学报, 1995(4): 106-111.
[5]金恂叔.航天器研制中的环境是演及其发展趋势(上).环境技术, 2001(5): 16-18.
[6]金恂叔.航天器研制中的环境是演及其发展趋势(下).环境技术, 2001(6): 27-34.
[7]冯培德.可靠性工程技术在航空惯性导航系统研制中的应用.中国惯性技术学报, 1996, 4(4): 56-65.
[8]张士峰. Bayes可靠性评估方法述评.飞行器测控学报, 2000, 19(2): 28-34.
[9] William Denson. The history of reliability prediciton. IEEE Transactions on Reliability, 1998, 47(3): 321-328.
[10]韩庆田.可靠性预计及其发展趋向.电子标准化与质量, 2001(5): 7-9.
[11] J. S. Bendat and A. G. Piersol.相关分析和谱分析的工程应用,凌福根译.北京:国防工业出版社, 1983.
[12]胡广书.数字信号处理,北京:清华大学出版社, 1988.
[13] Xing-Qi Jiang and Genshiro Kitagawa. Time varying coefficient vector AR modeling of nonstationary covariance time series. Signal Processing, 1993, 33(3): 315-331.
[14] Y. Zheng, D. B. H. Tay and Z. Lin. Modeling general distributed nonstationary process and identifying time-varying autoregressive system by wavelets: Theory and application. Signal Processing, 2001, 81(9): 1823-1848.
[15] Edgar F. Velez and Richard G. Absher. Parametric modeling of the Wigner half-kernel and its application to spectral estimate. Signal Processing, 1992, 26(2): 161-175.
[16] F. Shen, M. Zheng, D. Feng Shi, et al. Using the cross-correlation technique to extract modal parameters on response-only data. Journal of Sound and Vibration, 2003, 259(5): 1163-1179.
[17] R. J. Martin. Autoregression and irregular sampling: spectral estimation. Signal Processing, 1999, 77(2): 139-157.
[18] G. Meltzer and Nguyen Phong Dien. Fault diagnosis in gears operating under non-stationary rotational speed using polar wavelet amplitude maps. Mechanical Systems and Signal Processing, 2004, 18(5): 985-992.
[19] Wilson Q. Wang, M. Farid Golnaraghi and Fathy Ismail. Prognosis of machine health condition using neuro-fuzzy systems. Mechanical Systems and Signal Processing, 2004, 18(4): 813-831.
[20] Z. K. Peng and F. L. Chu. Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography. Mechanical Systems and Signal Processing, 2004, 18(2): 199-221.
[21] Z. Peng, F. Chu and Y. He. Vibration signal analysis and feature extraction based on reassigned wavelet scalogram. Journal of Sound and Vibration, 2002, 253(5): 1087-1100.
[22] N. E. Huang, Z. Shen, S. R. Long, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, 1998, 454(1971): 903-995.
[23]钟佑明.一种振动信号新变换法的研究.振动工程学报, 2002, 15(2): 233-238.
[24] Norden E. Huang, Zheng Shen and Steven R. Long. A new view of nonlinear water waves: The Hilbert spectrum. Annual Review of Fluid Mechanics, 1999, 31: 417-457.
[25] H. T. Vicent. Damage detection using empirical mode decomposition method and a comparison with wavelet analysis. in: Proceedings of the Second International Workshop on Structural Health Monitoring, Stanford, 1998: 891-900.
[26] Paul A. Hwang, Norden E. Huang and David W. Wang. A note on analyzing nonlinear and nonstationary ocean wave data. Applied Ocean Research, 2003, 25(4): 187-193.
[27] A. D. Veltcheva and C. Guedes Soares. Identification of the components of wave spectra by the Hilbert Huang transform method. Applied Ocean Research, 2004, 26(1-2): 1-12.
[28] D. J. Yu, J. S. Cheng and Y. Yang. Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings. Mechanical Systems and Signal Processing, 2005, 19(2): 259-270.
[29] G. H. Gai. The processing of rotor startup signals based on empirical mode decomposition. Mechanical Systems and Signal Processing, 2006, 20(1): 222-235.
[30]宋世斌.一种威布尔寿命分布模型.电子产品可靠性与环境试验, 2001(1): 7-9.
[31] Amos E. Gera. Modified exponentiated-Weibull distribution for life-time modeling. Philadelphia, PA, USA, 1997149-152.
[32] G. Pulcini. An exponential reliability-growth model in multicopy testing program. Ieee Transactions on Reliability, 2001, 50(4): 365-373.
[33] Kevin Fitzgibbon, Ron Barker, Tige Clayton, et al. A failure-forecast method based on Weibull and statistical-pattern analysis. Seattle, WA, 2002516-521.
[34] Lev M. Klyatis, Oleg I. Teskin and James W. Fulton. Multi-variate Weibull model for predicting system-reliability, from testing results of the components. Proceedings of the Annual Reliability and Maintainability Symposium, 2000: 144-149.
[35] John S. Usher. Weibull component reliability-prediction in the presence of masked data. IEEE Transactions on Reliability, 1996, 45(2): 229-232.
[36] Alexander Filippenko Apex. Vibration analysis for predictive maintenance of rotating machines, Patent No.: US 6370957B1, Apr. 16, 2002.
[37]布洛克威尔(美国),田铮译.时间序列的理论与方法(第二版),高等教育出版社, 2001.
[38]顾岚.时间序列分析,中国统计出版社, 1994.
[39]徐峰. AR模型应用于振动信号趋势预测的研究.清华大学学报(自然科学版), 1999, 39(4): 57-59.
[40]阎长俊.用AR模型预测机械故障的方法.沈阳建筑工程学院学报, 1995, 11(2): 138-141.
[41] Joseph E. Killpatrick. Laser gyro life prediction, Patent No.: US005719675A, Feb. 17, 1998.
[42]周长银.非线性Bayes动态模型预测的数值算法.经济数学, 2001(3): 58-61.
[43]周源泉.极值分布参数、可靠寿命、可靠性的Bayes精确区间估计.导弹与航天运载技术, 1997(4): 55-62.
[44]唐雪梅.具有不确定数据的指数可靠性增长的Bayes方法.航天控制, 1997(1): 64-68.
[45]张孝令.贝叶斯动态模型及其预测,济南:山东科学技术出版社, 1992.
[46] J. Rene van Dorp, Thomas A. Mazzuchi, Gordon E. Fornell, et al. Bayes approach to step-stress accelerated life testing. IEEE Transactions on Reliability, 1996, 45(3): 491-498.
[47] Wayne Nelson. ACCELERATED LIFE TESTING EM DASH STEP-STRESS MODELS AND DATA ANALYSES. IEEE Transactions on Reliability, 1980, R-29(2): 103-108.
[48] Robert Miller and Wayne Nelson. OPTIMUM SIMPLE STEP-STRESS PLANS FOR ACCELERATED LIFE TESTING. IEEE Transactions on Reliability, 1983, R-32(1):59-65.
[49] D. S. Bai and Y. R. Chun. Optimum simple step-stress accelerated life-tests with competing causes of failure. IEEE Transactions on Reliability, 1991, 40(5): 622-627.
[50] J. Rene Van Dorp, Thomas A. Mazzuchi and Jorge E. Garciduenas. A comparison of accelerated life testing designs within A single Bayesian inferential framework. Newport Beach, CA, United States, 2006208-214.
[51] William J. Zimmer and John J. Deely. Bayes ranking of survival distributions using accelerated or correlated data. IEEE Transactions on Reliability, 1996, 45(3): 499-504.
[52] David Robinson and Duane Dietrich. Nonparametric-Bayes reliability-growth model. IEEE Transactions on Reliability, 1989, 38(5): 591-598.
[53] E. P. Virene. Reliability growth and its upper limit. in: Proceedings of Annual Reliability and Maintainability Symposium, 1968, 265-270.
[54] D. Robinson and D. Dietrich. A New Nonparametric Growth Model. IEEE Transactions on Reliability, 1987, 36: 411-418.
[55] C. A. Clarotti and F. Spizzichino. Bayes predictive approach in reliability theory. IEEE Transactions on Reliability, 1989, 38(3): 379-382.
[56] Aric Dumai and Avi Winkler. Relaiblity prediction model for gyroscope. in: Proceedings Annual Reliability and Maintainability Symposium, 1990, 5-9.
[57]邓聚龙.灰色控制系统,华中工学院出版社, 1985.
[58]邓聚龙.灰色系统理论教程,华中理工大学出版社, 1990.
[59]任玉笋.灰色系统理论、数据预处理及其应用.上海交通大学学报, 2001, 35(2): 268-271.
[60]樊春玲.长寿命动调陀螺仪的寿命预测方法研究,上海交通大学,Ph.D. thesis, 2004.
[61]杨江天.灰色模型在机械故障预测中的应用.机械强度, 2001, 23(3): 277-279.
[62] Jacek M. Zurada. Introduction to Artificial Neural Systems, New York, Los Angeles, San Francisco: West Publishing Company, 1992.
[63]陈增强.基于神经网络的非线性预测控制综述.控制工程, 2002, 9(4): 7-11.
[64] A. G. Parlos, O. T. Rais and A. F. Atiya. Multi-step-ahead prediction using dynamic recurrent neural networks. Neural Networks, 2000, 13(7): 765-786.
[65] Hong-Te Su, Thomas J. McAvoy and Paul Werbos. Long-term predictions of chemical processes using recurrent neural networks. A parallel training approach. Industrial & Engineering Chemistry Research, 1992, 31(5): 1338-1352.
[66] Guy-Michel Cloarec and John Ringwood. Incorporation of statistical methods inmulti-step neural network prediction models. Anchorage, AK, USA, 1998, 3, pp. 2513-2518.
[67] A. G. Parlos, K. T. Chong and A. F. Atiya. Application of the neural multiplayer perception in modeling complex process dynamics. IEEE Transactions on Neural Netoworks: Sepcial Issue on Recurrent Dynamic Neural networks, 1994, 5(2): 255-266.
[68]潘紫微.基于神经网络的多特征和多步轴承寿命预测方法.机械科学与技术, 1999, 18(4): 584-586.
[69]曾令可.人工神经网络及其在硅酸铝纤维寿命预测中的应用.中国陶瓷工业, 2000, 7(3): 28-31.
[70] V. N. Vapnik. The nature of statistical learning theory, New York: Springer, 1995.
[71] A. J. Smola and B. Scholkopf. A tutorial on support vector regression. Statistics and Computing, 2004, 14(3): 199-222.
[72] Steve R. Gunn. Support vector machines for classification and regression, Technical Report, University of Southampton, 1998.
[73] Haisheng Li and Xuefeng Zhu. Application of support vector machine method in prediction of kappa number of kraft pulping process. Hangzhou, China, 2004, 4, pp. 3325-3330.
[74] Sayan Mukherjee, Edgar Osuna and Federico Girosi. Nonlinear prediction of chaotic time series using support vector machines. Amelia Island, FL, USA, 1997511-520.
[75] F. E. H. Tay and L. J. Cao. Application of support vector machines in financial time series forecasting. Omega-International Journal of Management Science, 2001, 29(4): 309-317.
[76] J. I. Yeh and L. S. Mao. Prediction of membrane proteins in Mycobacterium tuberculosis using a Support Vector Machine algorithm. Journal of Computational Biology, 2006, 13(1): 126-129.
[77] D. P. Cui and D. Curry. Prediction in marketing using the support vector machine. Marketing Science, 2005, 24(4): 595-615.
[78]胡金海.基于支持向量机方法的发动机性能趋势预测.推进技术, 2005, 26(3): 260-264.
[79]李凌均.基于支持向量机的机械设备状态趋势预测研究.西安交通大学学报, 2004, 38(3): 230-234.
[80] F. E. H. Tay and L. J. Cao. Modified support vector machines in financial time series forecasting. Neurocomputing, 2002, 48: 847-861.
[81] L. J. Cao and F. E. H. Tay. Support vector machine with adaptive parameters in financial time series forecasting. Ieee Transactions on Neural Networks, 2003, 14(6): 1506-1518.
[1] V. N. Vapnik. The nature of statistical learning theory, New York: Springer-Verlag, 1995.
[2]安金龙.支持向量机若干问题的研究,天津大学, Ph.D. thesis, 2004.
[3] S.R. Gunn. Support vector machines for classification and regression. Technical Report ISIS-1-98, University of Southampton, 1998.
[4]范昕炜.支持向量机算法的研究及其应用,浙江大学, Ph.D. thesis, 2003.
[5] V. N. Vapnik. An overview of statistical learning theory. Ieee Transactions on Neural Networks, 1999, 10(5): 988-999.
[6] Y. S. Abu-Mostafa. The Vapnik-Chervonenkis dimension: information versus complexity in learning. Neural Computation, 1989, 1(3): 312-317.
[7] P. Koiran and E. D. Sontag. Vapnik-Chervonenkis dimension of recurrent neural networks. Discrete Applied Mathematics, 1998, 86(1): 63-79.
[8] M. Schmitt. On using the Poincare polynomial for calculating the VC dimension of neural networks. Neural Networks, 2001, 14(10): 1465-1465.
[9] Vladimir Vapnik, Esther Levin and Yann Le Cun. Measuring the VC-dimension of a learning machine. Neural Computation, 1994, 6(5): 851.
[10] C. J. C. Burges. A tutorial on Support Vector Machines for pattern recognition. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167.
[11] J. Mercer. Functions of positive and negative type and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society of London, Series A, 1909, 209: 415-446.
[12] A. J. Smola and B. Scholkopf. A tutorial on support vector regression. Statistics and Computing, 2004, 14(3): 199-222.
[1]周百令.动力调谐陀螺仪设计与制造,南京:东南大学出版社, 2002.
[2] PC-Labcard. PCL-818L User's Manual, Advantech corporation, 1995.
[3]田自耘.陀螺仪漂移测试基础,北京:国防工业出版社, 1998.
[4] Gerard N. Stenbakken and Amos Dolev. High-accuracy sampling wattmeter. IEEE Transactions on Instrumentation and Measurement, 1992, 41(6): 974-978.
[5] Gerard N. Stenbakken. A wideband sampling wattmeter. IEEE Transactions on Power Apparatus and System, 1984, 103(10): 2919-2926.
[6] David J. Kruglinski. Visual C++技术内幕(第四版),北京:清华大学出版社, 1999.
[7]侯俊杰.深入浅出MFC(第二版),武汉:华中科技大学出版社, 2001.
[8] Jeffrey Richter. Windows核心编程,北京:机械工业出版社, 2000.
[9]钱能. C++程序设计教程,北京:清华大学出版社, 1999.
[1]余勇,动力调谐陀螺温度漂移的建模研究,东南大学, Ph.D. thesis, 2000.
[2] J. L. Deng. Introduction to grey system theory. The Journal of Grey System, 1989, 1(1): 1-24.
[3] Zhijie Chen, Weizhen Chen, Qile Chen, et al. Trend relational analysis and grey-fuzzy clustering method. Bethesda, MD, United States, 2006, pp. 234-245.
[4] M. Lu and K. Wevers. Application of grey relational analysis for evaluating road traffic safety measures: Advanced driver assistance systems against infrastructure redesign. IET Intelligent Transport Systems, 2007, 1(1): 3-14.
[5] K. C. Chang and M. F. Yeh. Grey relational analysis based approach for data clustering. IEE Proceedings: Vision, Image and Signal Processing, 2005, 152(2): 165-172.
[6] G. P. Xu, W. F. Tian, L. Qian, et al. A novel conflict reassignment method based on grey relational analysis (GRA). Pattern Recognition Letters, 2007, 28(15): 2080-2087.
[7] Anton Plotkin and Eugene Paperno. Compensation of temperature-drift errors with no additional hardware. IEEE Instrumentation and Measurement Magazine, 2007, 10(5): 20-25.
[8]诸一江.陀螺仪温度模型建模方法介绍.中国惯性技术学报, 1995, 3(1): 32-37.
[9]郝玉红.温度变化引起的静压液浮陀螺漂移的补偿研究.惯导与仪表, 1996, 2: 51-56.
[10]王淑娟.加速度计温度模型辨识.中国惯性技术学报, 1997, 5(1): 31-36.
[11] Qing Dong Yang, Christophe Van den Bergh, Paul Vanherck, et al. Linear regression and neural net models applied to thermal error compensation in machine tools. European Journal of Mechanical and Environmental Engineering, 1999, 44(3): 146-152.
[12] Jiun-Hong Lai and Chin-Teng Lin. Application of neural fuzzy network to pyrometer correction and temperature control in rapid thermal processing. IEEE Transactions on Fuzzy Systems, 1999, 7(2): 160-175.
[13] Christopher D. Mize and John C. Ziegert. Neural network thermal error compensation of a machining center. Precision Engineering, 2000, 24(4): 338-346.
[14] J. M. Dias Pereira, Octavian Postolache, P. M. B. Girao, et al. Minimizing temperature drift errors of conditioning circuits using artificial neural networks. IEEE Transactions on Instrumentation and Measurement, 2000, 49(5): 1122-1127.
[15] S. Yang, J. Yuan and J. Ni. Improvement of thermal error modeling and compensation onmachine tools by CMAC neural network. International Journal of Machine Tools & Manufacture, 1996, 36(4): 527-537.
[16] H. Yang and J. Ni. Dynamic neural network modeling for nonlinear, nonstationary machine tool thermally induced error. International Journal of Machine Tools & Manufacture, 2005, 45(4-5): 455-465.
[17] R. Ramesh, M. A. Mannan and A. N. Poo. Thermal error measurement and modelling in machine tools.: Part I. Influence of varying operating conditions. International Journal of Machine Tools and Manufacture, 2003, 43(4): 391-404.
[18] R. Ramesh, M. A. Mannan, A. N. Poo, et al. Thermal error measurement and modelling in machine tools. Part II. Hybrid Bayesian Network - Support vector machine model. International Journal of Machine Tools and Manufacture, 2003, 43(4): 405-419.
[19] G. P. Xu, W. F. Tian, Z. H. Jin, et al. Temperature drift modelling and compensation for a dynamically tuned gyroscope by combining WT and SVM method. Measurement Science & Technology, 2007, 18(5): 1425-1432.
[20] David L. Donoho. De-noising by soft-thresholding. IEEE Transactions on Information Theory, 1995, 41(3): 613-627.
[21] Chau-Jern Cheng and Li-Chien Lin. Optical wavelet subband filtering for joint transform correlation. Optical Engineering, 2005, 44(10): 105001.
[22] James E. Fowler. The redundant discrete wavelet transform and additive noise. IEEE Signal Processing Letters, 2005, 12(9): 629-632.
[23] S. Mallat. A theory for multi-resolution decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Machine Intell., 1989, 11(7): 674-693.
[24] I. Daubechies. The wavelet transform time-frequency localization and signal analysis. IEEE Trans. on Information Theory, 1990, 36(5): 961-1005.
[25] D. Sko?aj, Robust subspace approaches to visual learning and recognition. 2003: Computer and Information Science, University of Ljubljana, Ph.D. thesis.
[26] Chanchal Chatterjee, Zhengjiu Kang and Vwani P. Roychowdhury. Algorithms for accelerated convergence of adaptive PCA. IEEE Transactions on Neural Networks, 2000, 11(2): 338-355.
[27] Vo Dinh Minh Nhat and Sung Young Lee. Two-dimensional weighted PCA algorithm for face recognition. Espoo, Finland, 2005219-223.
[28] Masaaki Shikada, Noboru Muramatu, Takashi Kusaka, et al. Extraction of landslide areas using satellite remote sensing and GIS technology. in: International Geoscience andRemote Sensing Symposium (IGARSS), Italy, 1995, 1, pp. 377-379.
[29] N. Louw and S. J. Steel. Variable selection in kernel Fisher discriminant analysis by means of recursive feature elimination. Computational Statistics and Data Analysis, 2006, 51(3): 2043-2055.
[30] Ha-Nam Nguyen and Syng-Yup Ohn. DRFE: Dynamic recursive feature elimination for gene identification based on random forest. Lecture Notes in Computer Science, 2006, 4234 NCS - III: 1-10.
[31]薛毅,支持向量机与数学规划,北京工业大学, Ph.D. thesis, 2003.
[32] F. E. Sistler. Grading agriculture products with machine visio. IEEE International Workshop on Intelligent Robots and System, 1990: 255-261.
[33]任东,基于支持向量机的植物病害识别研究,吉林大学, Ph.D. thesis, 2007.
[34] A. F. Apex, S. B. Wake Forest and A. N. Morrisville, Vibration analysis for predictive maintenance of rotating machines. 2002: United States Patent: US6370957B1.
[35] Runqing Huang, Lifeng Xi, Xinglin Li, et al. 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.
[36] G. H. Gai. The processing of rotor startup signals based on empirical mode decomposition. Mechanical Systems and Signal Processing, 2006, 20(1): 222-235.
[37] N. Gebraeel, M. Lawley, R. Liu, et al. Residual life, predictions from vibration-based degradation signals: A neural network approach. Ieee Transactions on Industrial Electronics, 2004, 51(3): 694-700.
[1] D. Gabor. Theory of communication. J. IEE, 1946, 93(26): 429-457.
[2] G. Meltzer and Y. Y. Ivanov. Fault detection in gear drives with non-stationary rotational speed-part I: The time-frequency approach. Mechanical Systems and Signal Processing, 2003, 17(5): 1033-1047.
[3] S. A. Neild, P. D. McFadden and M. S. Williams. A review of time-frequency methods for structural vibration analysis. Engineering Structures, 2003, 25(6): 713-728.
[4] J. Zou and J. Chen. A comparative study on time-frequency feature of cracked rotor by Wigner-Ville distribution and wavelet transform. Journal of Sound and Vibration, 2004, 276(1-2): 1-11.
[5] F. L. Di Scalea and J. McNamara. Measuring high-frequency wave propagation in railroad tracks by joint time-frequency analysis. Journal of Sound and Vibration, 2004, 273(3): 637-651.
[6] J. Kim, D. E. Welcome, R. G. Dong, et al. Time-frequency characterization of hand-transmitted, impulsive vibrations using analytic wavelet transform. Journal of Sound and Vibration, 2007, 308(1-2): 98-111.
[7] N. E. Huang, Z. Shen, S. R. Long, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, 1998, 454(1971): 903-995.
[8] N. E. Huang, M. L. C. Wu, S. R. Long, et al. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proceedings of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, 2003, 459(2037): 2317-2345.
[9] H. T. Vicent. Damage detection using empirical mode decomposition method and a comparison with wavelet analysis. in: Proceedings of the Second International Workshop on Structural Health Monitoring, Stanford, 199891-900.
[10] G. H. Gai. The processing of rotor startup signals based on empirical mode decomposition. Mechanical Systems and Signal Processing, 2006, 20(1): 222-235.
[11] D. J. Yu, J. S. Cheng and Y. Yang. Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings. Mechanical Systems and Signal Processing, 2005, 19(2): 259-270.
[12]熊卫华,经验模态分解方法及其在变压器状态监测中的应用研究,浙江大学,Ph.D. thesis, 2006.
[13]赵治栋.基于Hilbert-Huang Transform的心音信号谱分析.传感技术学报, 2005, 18(1): 18-22.
[14]杨宇,基于EMD和支持向量机的旋转机械故障诊断方法研究,湖南大学,Ph.D. thesis, 2005.
[15] G. Xu, W. Tian and L. Qian. EMD- and SVM-based temperature drift modeling and compensation for a dynamically tuned gyroscope (DTG). Mechanical Systems and Signal Processing, 2007, 21(8): 3182-3188.
[16] B. Xuan, Q. W. Xie and S. L. Peng. EMD sifting based on bandwidth. Ieee Signal Processing Letters, 2007, 14(8): 537-540.
[17] Leon Cohen. Time-Frequency Analysis: Theory and Application, New York: Prentice Hall, 1995.
[18] S. Olhede and A. T. Walden. A generalized demodulation approach to time-frequency projections for multicomponent signals. in: Proceedings of the Royal Society a-Mathematical Physical and Engineering Sciences, 2005, 461, pp. 2159-2179.
[19] N. E. Huang, Computer implicated empirical mode decomposition method, apparatus, and article of manufacture. 1996: U.S. Patent Pending.
[20]陈忠. EMD信号分析方法边缘效应的分析.数据采集与处理, 2003, 18(1): 114-118.
[21] D. J. Huang, J. P. Zhao and J. L. Su. Practical implementation of Hilbert-Huang Transform algorithm. Acta Oceanologica Sinica, 2003, 22(1): 1-14.
[22]张郁山.利用自回归模型处理EMD方法中的边界问题.自然科学进展, 2003, 13(10): 1054-1059.
[23] Y. J. Deng, W. Wang, C. C. Qian, et al. Boundary-processing-technique in EMD method and Hilbert transform. Chinese Science Bulletin, 2001, 46(11): 954-961.
[24] M. J. D. Powell, Radial basis functions for multivariable interpolation: Review IMA conf. on algorithms for the approximation of functions data, RMCS Shrivanham, 1985.
[25] D. S. Broomhead and D. Lowe. Multivariate functional interpolation and adaptive networks Complex Systems, 1988, 2: 321-355.
[26] S. Chen, C. F. N. Cowan and P. M. Grant. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks, 1991, 2(2): 302-309.
[27] E. S. Chng, S. Chen and B. Mulgrew. Gradient radial basis function networks fornonlinear and nonstationary time series prediction. IEEE Transactions on Neural Networks, 1996, 7(1): 190-194.
[28] M. V. Wickerhauser. Adapted wavelet analysis from theory to software, New York: IEEE Press, 1994.
[29] A. F. Apex, S. B. Wake Forest and A. N. Morrisville, Vibration analysis for predictive maintenance of rotating machines. 2002: United States Patent: US6370957B1.
[30] Y. Shao and K. Nezu. Prognosis of remaining bearing life using neural networks. Proceedings of the Institution of Mechanical Engineers Part I-Journal of Systems and Control Engineering, 2000, 214(13): 217-230.
[31]李书明,制造设备智能诊断与状态预测技术的研究,天津大学,Ph.D. thesis, 1998.
[32] R. Q. Huang, L. F. Xi, X. L. Li, et al. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mechanical Systems and Signal Processing, 2007, 21: 193-207.
[33] D. Ho and R. B. Rand. Optimization of bearing diagnostic techniques using simulated and actual bearing fault signal. Mechanical Systems and Signal Processing, 2000, 14(5): 763-788.
[34] M. J. Boek. Experiments in the application of neural networks to rotating machine fault diagnosis. in: Proceedings of IEEE International Joint Conference on Neural Networks, 1991, 1, pp. 769-774.
[35] N. Gebraeel, M. Lawley, R. Liu, et al. Residual life, predictions from vibration-based degradation signals: A neural network approach. Ieee Transactions on Industrial Electronics, 2004, 51(3): 694-700.
[36]侯军虎,基于多参数的风机状态监测与故障诊断的研究,华北电力大学,Ph.D. thesis, 2003.
[37] X. Zhang and W. H. Wong, Recursive sample classification and gene selection based on SVM: method and software description. 2001: Technical Report, Dept. of Biostatistics, Harvard School of Public Health.
[1] Hui-Kuo Chang, Hsing-Chia Kuo and Yen-Zen Wang. Novel grey model for diesel engine oil monitoring. Journal of Ship Research, 2006, 50(1): 31-37.
[2] T. H. M. El-Fouly, E. F. El-Saadany and M. M. A. Salama. Grey predictor for wind energy conversion systems output power prediction. IEEE Transactions on Power Systems, 2006, 21(3): 1450-1452.
[3] Xue-Liang Ping, Sheng-Lan Liu, Jin-Liang Li, et al. Application of grey prediction for predicting the measuring point for a CMM. International Journal of Advanced Manufacturing Technology, 2007, 32(3-4): 288-292.
[4] Tzu-Li Tien. The indirect measurement of tensile strength of material by the grey prediction model GMC(1, n). Measurement Science and Technology, 2005, 16(6): 1322-1328.
[5] Min Han, Jianhui Xi, Shiguo Xu, et al. Prediction of chaotic time series based on the recurrent predictor neural network. IEEE Transactions on Signal Processing, 2004, 52(12): 3409-3416.
[6] Runqing Huang, Lifeng Xi, Xinglin Li, et al. 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.
[7] Raymond S. T. Lee. iJADE stock advisor: An intelligent agent based stock prediction system using hybrid RBF recurrent network. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans., 2004, 34(3): 421-427.
[8] A. K. Singh, S. S. Panda, D. Chakraborty, et al. Predicting drill wear using an artificial neural network. International Journal of Advanced Manufacturing Technology, 2006, 28(5-6): 456-462.
[9] E. Varoglu and K. Hacioglu. Recurrent neural network speech predictor based on dynamical systems approach. IEE Proceedings: Vision, Image and Signal Processing, 2000, 147(2): 149-156.
[10] V. N. Vapnik. The nature of statistical learning theory, New York: Springer-Verlag, 1995.
[11] C. Nello and S. T. John.支持向量机导论,电子工业出版社, 2004.
[12] L. J. Cao and F. E. H. Tay. Support vector machine with adaptive parameters in financial time series forecasting. Ieee Transactions on Neural Networks, 2003, 14(6): 1506-1518.
[13] F. E. H. Tay and L. J. Cao. Application of support vector machines in financial time series forecasting. Omega-International Journal of Management Science, 2001, 29(4): 309-317.
[14] F. E. H. Tay and L. J. Cao. Modified support vector machines in financial time series forecasting. Neurocomputing, 2002, 48: 847-861.
[15] Ciwei Gao, Ettore Bompard, Roberto Napoli, et al. Price forecast in the competitive electricity market by support vector machine. Physica A: Statistical Mechanics and its Applications, 2007, 382(1): 98-113.
[16] Mohamed Mohandes. Support vector machines for short-term electrical load forecasting. International Journal of Energy Research, 2002, 26(4): 335-345.
[17] Ping-Feng Pai and Wei-Chiang Hong. Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Conversion and Management, 2005, 46(17): 2669-2688.
[18] Bo-Juen Chen, Ming-Wei Chang and Chih-Jen Lin. Load forecasting using support vector machines: A study on EUNITE Competition 2001. IEEE Transactions on Power Systems, 2004, 19(4): 1821-1830.
[19] G. P. Xu, W. F. Tian and Z. H. Jin. An AGO-SVM drift modelling method for a dynamically tuned gyroscope. Measurement Science & Technology, 2006, 17(1): 161-167.
[20] J. L. Deng. Introduction to grey system theory. The Journal of Grey System, 1989, 1(1): 1-24.
[21]邓聚龙.灰色理论基础,武汉:华中科技大学出版社, 2002.
[22] H. T. Lu, W. J. Kolarik and S. S. Lu. Real-time performance reliability prediction. Ieee Transactions on Reliability, 2001, 50(4): 353-357.
[23] S. Lu, H. Lu and W. J. Kolarik. Multivariate performance reliability prediction in real-time. Reliability Engineering & System Safety, 2001, 72(1): 39-45.
[24] C. Chatfield, A. B. Koehler, J. K. Ord, et al. A new look at models for exponential smoothing. Journal of the Royal Statistical Society Series D-the Statistician, 2001, 50: 147-159.
[25] F. L. Greitzer and T. A. Ferryman. Predicting remaining life of mechanical systems. in: Intelligent Ship Symposium IV, 2001.