用户名: 密码: 验证码:
基于性能参数的数控装备服役可靠性评估方法与应用
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
国产数控装备的可靠性是制约数控装备行业发展的重大共性和关键性工程问题之一。作为一种对数控装备可靠性进行定量控制的必要手段,数控装备服役可靠性评估的主要任务是衡量数控装备是否达到预期的设计目标及使用要求,指出数控装备运行中的薄弱环节,为改进数控装备的设计、制造、工艺与维护等指明方向,从而实现数控装备的可靠性增长,保证数控装备长期安全可靠运行。本文根据数控装备的固有特征,分别从理论方法、工程应用、软件系统三个层面对数控装备服役可靠性评估进行了较为深入系统地研究与探索。
     首先,综述了目前数控装备可靠性研究及其相关领域的理论方法和研究现状,指出传统的基于二元状态(正常和故障)的可靠性评估研究的诸多缺陷。在此基础上,定义数控装备服役可靠性评估的内涵,给出数控装备服役可靠性指标,提出数控装备服役可靠性评估的核心内容与方法。构建数控装备服役可靠性评估的三层结构,并论述数控装备的性能状态监测与加工过程动力学建模方法。
     围绕着数控装备服役可靠性评估的三层结构,开展基于性能参数的数控装备服役可靠性评估研究,具体包括:
     (1)分析了数控装备使用过程中的性能劣化现象,考虑性能状态变化特征,提出基于性能状态监测的数控装备性能劣化建模与分析的原理。建立基于非平稳ARIMA时间序列法的数控装备性能劣化模型,探索数控装备的性能劣化时变性规律。引入离散隐Markov链分析多观测序列下数控装备可靠性变动状况,较好地解决了数控装备使用过程中所观测到的性能特征参数不能与性能状态——对应的问题。在此过程中,采用Lloyd算法实现性能特征参数的矢量量化,建立性能劣化的状态变迁模型及其可靠性变动计算模型,该方法能够准确地感知数控装备隐状态变迁过程,并能推断数控装备完成规定功能的概率以及处于不同状态下的概率。
     (2)分析了传统方法应用于数控装备服役可靠性评估时的困境,考虑数控装备结构特性和加工工艺特征等因素,采用动力学建模手段,分析数控装备处于某一性能状态时不同因素对数控装备输出性能的影响,提出小样本条件下数控装备服役可靠性评估的新思路。引入统计学习理论(SLT),建立数控装备服役可靠性评估模型,并给出相应的模型选择算法和模型求解算法,得到数控装备在某一性能状态下的概率型可靠性指标。在此基础上,将Bootstrap与SLT相结合进行可靠性置信区间估算,并对评估结果进行可靠性敏感度和重要度分析,以发现影响服役可靠性的最敏感因素以及不同单元对系统服役可靠性影响的重要度顺序等。该方法较好地解决了小样本条件下数控装备服役可靠性评估中非线性、评估准确度低等问题,提高了数控装备服役可靠性评估模型的稳健性和准确性。
     (3)以数控DM4600型立式铣床为研究对象,通过结构综合动刚度实验和铣削力实验,得到相应的动力学模型参数和铣削力模型参数,建立该装备的铣削加工过程非线性动力学模型,并进行实验校验。在此基础上,采用仿真手段分析数控装备机械结构与工艺系统的动态特性对数控装备铣削加工输出性能的影响,进而对数控装备在一个加工周期内的服役可靠性进行了评估。
     (4)自主设计与开发了数控装备可靠性建模与评估(MRME)原型软件系统,介绍系统的功能模块及特点,并以具体的工程应用实例为例演示MRME系统的运行过程。
     最后,对全文进行总结,指出进一步的研究方向。
The reliability of Made-in-China CNC equipment is one of the key problems to restrict the rapid development of CNC equipment industry. The operational reliability estimation for a CNC equipment is an indispensable approach to control the reliability level of the equipment, and its tasks are to ensure that the CNC equipment will achieve desired objectives and requirements, and to identify those underlying faults, and then to indicate the required improvements in the CNC equipment's design, manufacture, operation, maintenance and etc., in order to implement reliability growth and improve reliability level. According to current deficiency research on operational reliability estimation for CNC equipment, methods, applications and a developed prototype system for the operational reliability estimation are discussed in detail based on the inherent characteristics of CNC equipment.
     Firstly, this thesis provides a systematic literature review on the theories and methods for CNC equipment reliability and its related fields, and points out the limitations of traditional reliability estimation based on binary states: working or failed. Therefore, the concept of operational reliability estimation is brought forward. The reliability index and main research content of operational reliability estimation for the CNC equipment are built. And two failure modes such as catastrophic failure and drift failure are analyzed. And then, the three-tier system structure of operational reliability estimation is set up. Meanwhile, the methods of performance monitoring and machining processes' dynamic modeling in CNC equipment are presented.
     According to the three-tier system structure of operational reliability estimation, the operational reliability estimation for CNC equipment reliability is implemented as follows:
     (1) The symptoms of performance degradation related to CNC equipment are illuminated, and the methods of performance degradation modeling and analysis based on performance monitoring are proposed. The performance degradation models based on nonsteady ARIMA time series are set up, which might correctly describe performance degradation process. Furthermore, discrete hidden Markov chain model is introduced to analyze the CNC equipment reliability under conditions of several observation sequences. The result shows that the discrete hidden Markov chain model might recognize the hidden states change of degradation process.
     (2) After indicating the difficulties in applying traditional reliability estimation methods to the CNC equipment, it is brought forward that the approach of the operational reliability estimation for the CNC equipment with small sample are based on statistical learning theory. The reliability estimation models are built using v-SVR and AIS arithmetic. And the confidence interval of calculated reliability index is deduced by integrating the Bootstrap with v-SVR arithmetic. Furthermore, the sensitivity and importance level of calculated reliability index are counted.
     (3) The foregoing methods are adopted to estimate the operational reliability of a machining process in DM4600 vertical milling machining center. The nonlinear dynamics model of the machining process is built based on the dynamic characteristic testing for the DM4600 machining center. Meanwhile, the model is verified by milling testing. The result shows that the nonlinear dynamics model is feasible. Furthermore, it is discussed how the dynamic characteristics of mechanical structure and machining system impact the operational reliability for the machining center. And the effect of machining parameter varieties on machining performance is studied by means of emulation. And then, the operational reliability for this equipment within a machining cycle is estimated.
     (4) The prototype system of machine reliability modeling and estimation called MRME is designed and developed. Its function modules and adoptive technologies are presented. A case is given to demonstrate the operation process of MRME system.
     Finally, a conclusion is drawn and the trend on operational reliability estimation is anticipated.
引文
[1]Patrick D.T.O'Connor.Practical Reliability Engineering,fourth Edition.Publisher:John Willey and Sons Ltd,2002.
    [2]贾亚洲.提高国产数控机床可靠性水平.数控机床市场,2006,5:92-99.
    [3]王超,王金等.机械可靠性工程.北京:冶金工业出版社,1992.
    [4]Barringer P.E.,The evolution of reliability.International Maintenance Conference,Florida,December 7-10,2003.
    [5]Saleh J.H.,Marais K.,highlights from the early(and pre-) history of reliability engineering.Reliability Engineering and System Safety,2006,91:249-256.
    [6]Patrick Q.C.,Standards in reliability and safety engineering.Reliability Engineering and System Safety,1998,60:173-177.
    [7]Ciappa M.,Carbognani F.,Fichtner W.,Lifetime prediction and design of reliability tests for high-power devices in automotive applications.IEEE Transactions on Device and Materials Reliability 2003,3(4):191-196.
    [8]Raizer V.,Theory of reliability in structural design.Applied Mechanics Reviews,2004,57(1):1-21.
    [9]Software R.,Reliability:A Practitioner's Guide.陈晓彤等译.北京:北京航空航天大学出版社,2005.
    [10]高社生,张玲霞.可靠性理论与工程应用.北京:国防工业出版社,2002.
    [11]普罗尼科夫A.C.等著.数控机床的精度与可靠性.李昌琪,遇立基译.北京:机械工业出版社,1987.
    [12]Theodor Freiheit,S.Jack Hu.Impact of Machining Parameters on Machine Reliability and System Productivity.Transactions of the ASME,2002,124:296-304.
    [13]John Berner.Calculating machine reliability from bearing life.Machine Design,2005,3:55-57.
    [14]Jin-Hyeon Lee,Seung-Han Yang.Fault diagnosis and recovery for a CNC machine tool thermal error compensation system.Journal of Manufacturing Systems,2001,19(6):428-434.
    [15]Jason R.W.Merrick,Refik Soyer,Thomas A.Mazzuchi.A bayesian semiparametric analysis of the reliability and maintenance of machine tools.Technometrics,2003,45(1):58-69.
    [16]M.Savsar.Reliability analysis of a flexible manufacturing cell.Reliability Engineering and System Safety,2000,67:147-152.
    [17]Way Kuo.An Annotated Overview of System-Reliability Optimization.IEEE Transactions on Reliability,2000,49(2):176-187.
    [18]张英芝,申桂香,贾亚洲等.数控车床故障分布规律及可靠性.农业机械学报,2006,37(1):156-159.
    [19]申桂香,王桂萍,贾亚洲等.面向网络的数控装备可靠性分析技术.中国机械工程,2005,16(1):33-41.
    [20]武小悦,沙基昌.柔性制造系统可靠性分析的GOOPN模型.计算机集成制造系统,2000,6(2):65-69.
    [21]张强,吴耀华,贾亚洲.面向并行工程的数控机床可靠性控制模型.机械工程学报,2001,37(7):26-29.
    [22]Joseph V.R.,Yu T.,Reliability improvement experiments with degradation data,IEEE Transactions on Reliability,2006,55(1):149-156.
    [23]Padgett W.J.and Tomlinson M.A.Inference from accelerated degradation and failure data based on Gaussian process models.Lifetime Data Analysis,2004,10:191-206.
    [24]Bae S.J.,Kuo W.,Kvam P.H.,Degradation models and implied lifetime distributions.Reliability Engineering and System Safety,2007,92,601-608.
    [25]Jayaram J.S.R.,Girish T.,Reliability prediction through degradation data modeling using a quasi-likelihood approach.Proc.Ann Reliability &Maintainability Symp.,2005,1993-199.
    [26]Wei Huang and Duane L.Dietrich.An alternative degradation reliability modeling approach using maximum likelihood estimation.IEEE Transaction on reliability,2005,54(2):310-317.
    [27]Bagdonavicius V.and Nikulin M.Estimation in degradation models with explanatory variables.Lifetime Data Analysis,2000,7:85-103.
    [28]Lawless J.and Crowder M.Covariates and random effects in a gamma process model with application to degradation and failure.Lifetime Data Analysis,2004, 10:213-227.
    [29]Park C.,Padgett W.J.Accelerated degradation models for failure based on geometric brownian motion and gamma processes.Lifetime Data Analysis,2005,11:511-527.
    [30]张永强,刘琦,周经伦.小子样条件下基于Normal-Poisson过程的性能可靠性评定.国防科技大学学报,2006,28(3):128-132.
    [31]Xue J.,Yang K.Dynamic reliability analysis of coherent multistate systems.IEEE Transactions on Reliability,1995,44(4):683-688.
    [32]Fawzan M.A.,Rahim A.A.,Optimal control of a deteriorating process with a quadratic loss function.Quality and Reliability Engineering International,2001,17,459-466.
    [33]Kopnov V.A.Optimal degradation process control by two-level policies.Reliability engineering and system safety,1999,66:1-11.
    [34]Zuo M.J.,Jiang R.,Yam R.C.M.Approaches for reliability modeling of continuous-state devices.IEEE Transactions on Reliability.1999;48(1):9-18.
    [35]Tseng S.T.,Tang J.,Ku I.H.Determination of bum-in parameters and residual life for highly reliable products.Naval Research Logistics,2003,50:1-14.
    [36]Lin J.C.,Lee K.J.and Hsu Y.L.,Bayesian Analysis of Box-Cox Transformed Linear Mixed Model with ARMA(p,q) Dependence.Journal of Statistical Planning and Inference,2005,133:435-451
    [37]Jayaram J.S.R.,Girish T.,Reliability prediction through degradation data modeling using aquasi-likelihood approach.Proceedings Annual Reliability and Maintainability Symposium,2005:193-199.
    [38]Shiau J.J.H.,Lin H.H.,Analyzing accelerated degradation data by nonparametric regression.IEEE Transactions on Reliability,1999,48(2):149-158.
    [39]Lu C.J.,Meeker W.Q.,Using degradation measures to estimate a time-to-failure distribution.Technometrics,1993,35(2):161-173.
    [40]Eghbali G.,and Elsayed E.A.,Reliability Estimate Using Degradation Data.In Advances in systems Science:Measurement,Circuits and Control,Electrical and Computer Engineering Series,WSES Press,2001:425-430.
    [41]Elsayed E.A.,Chen A.C.K.,Recent research and current issues in accelerated testing.IEEE International Conference on Systems,Man and Cybernetics 1998,5:4704-4709
    [42]Ebrahem M.A.H,Higgins J.J.Non-parametric analysis of a proportional wearout model for accelerated degradation data.Applied Mathematics and Computation,2006,174(1):365-373.
    [43]Qureshi F.S,Sheikh A.K.,A probabilistic characterization of adhesive wear in metals.IEEE Transactions on reliability,1997,46(1):38-44.
    [44]Lu J.C.,Park J.and Yang Q.,Statistical inference of a time-to-failure distribution derived from linear degradation data.Technometrices,1997,39(4):391-400.
    [45]Meeker W.Q.,Escobar L.A.,Lu C.J.,Accelerated degradation tests:Modeling and analysis.Technometrics,1998,40(2):89-99.
    [46]Crk V.,Component and system reliability assessment from degradation data.Doctor of Philosophy:The University of Arizona,1998.
    [47]Djurdjanovic D.,Lee J.,Ni J.,Watchdog Agent:an infotronics-based prognostics approach for product performance degradation assessment and prediction.Advanced Engineering Informatics,2003,17:109-125.
    [48]Lee Jay,Ni Jun,Dragan Djurdjanovic etc.Intelligent prognostics tools and e-maintenance.Computers in Industry,2006,57:476-489.
    [49]Chinnam R.B.,On-line reliability estimation for individual components using statistical degradation signal models.Quality and Reliability Engineering International,2002,18:53-73.
    [50]Lu S.,Tu Y.C.,Lu H.,Predictive condition-based maintenance for continuously deteriorating systems.Quality and Reliability Engineering International,2007,23:71-81.
    [51]Lu S.,Lu H.,Kolarik W.J.,Multivariate performance reliability prediction in real-time.Reliability Engineering and System Safety,2001,72:39-45.
    [52]邓爱民,陈循,张春华,汪亚顺.基于性能退化数据的可靠性评估,宇航学报,2006,27(3):546-552.
    [53]金光.一种综合性能与寿命数据的Bayes-Bootstrap方法.宇航学报,2007,28(3):731-734.
    [54]徐正国,周东华.基于马尔可夫链蒙特卡罗的实时可靠性预测方法研究,机械强度,2007,29(5):765-768.
    [55]李良巧.机械可靠性设计与分析.北京:国防工业出版社,1998.
    [56]Zhao J.,Tang J.,A generalized random variable approach for strain-based fatigue reliability analysis.Journal of Pressure Vessel Technology,2000,122(2):156-161.
    [57]Huang W.,Rronaland G.A.,A generalized SSl reliability model considering stochastic loading and strength aging degradation.IEEE Trans.On Reliability,2004,53(1):77-82.
    [58]孙权,赵建印,周经伦.复合应力作用下强度退化的应力-强度干涉模型可靠性统计分析.计算力学学报.2007,24(3):358-361
    [59]谢里阳,李翠玲.应力-强度干涉模型在系统失效概率分析中的应用及相关问题.机械强度.2005,27(4):492-497.
    [60]Fatemi A.,and Yang L.,Cumulative fatigue damage and life prediction theories:a survey of the state of the art for homogeneous materials.International Journal of Fatigue,1998,20(1):9-34.
    [61]Rao J.S.,Pathak A.and Chawla A.,Blade life:a comparison by cumulative damage theories.Journal of Engineering for Gas Turbines and Power,2001,123(4):886-892.
    [62]Khen R.and Altus E.,Micro-macro relations for fatigue crack growth.Mechanics of Materials,1995,19(2-3):89-101.
    [63]董聪.现代结构系统可靠性理论及其应用.北京:科学出版社,2001.
    [64]安伟光,赵维涛,严心池.不完整结构系统同时考虑强度和刚度的可靠性分析.工程力学.2005,22(4):58-61.
    [65]Lee E.T.,Wang J.W.,Statistical Methods for Survival Data Analysis.Technometrics,2003,45(4):372-373(2).
    [66]Marcorin A.J.,Abackerli A.J.,Field Failure Data:an Alternative Proposal for Reliability Estimation.Quality and Reliability Engineering International,2006,22(7):851-862.
    [67]Ghosh J.K.,Mohan Delampady,Tapas Samanta.An Introduction to Bayesian Analysis:Theory and Methods.Springer,2006.
    [68]Phillips M.J.,Bayesian estimation from censored data with incomplete information.Quality and Reliability Engineering International,2004,20:237-245.
    [69]John Quigley and Lesley Walls.Confidence intervals for reliability-growth models with small sample-sizes.IEEE Transactions on Reliability,2003,52(2):257-262.
    [70]陈文华,李奇志,张为鄂等.产品可靠性的Bootstrap区间估计方法.机械工 程学报,2003,39(6):106-109.
    [71]茆诗松,王玲玲,濮晓龙.威布尔分布场合无失效数据的可靠性分析.应用概率统计,1996,12(1):95-107.
    [72]张恒喜,郭基联,朱家元等.小样本多元数据分析方法及应用.西安:西北工业大学出版社,2002.
    [73]Paul H.G.,Joseph B.K.,Anthony O.H.,Statistic Al Methods for Eliciting Probability Distributions.Journal of the American Statistical Association,2005,100.
    [74]Pulcini G.,An exponential reliability-growth model in multi-copy testing program.IEEE Transactions on Reliability,2001,50(4):365-373.
    [75]康锐,王自力.可靠性系统工程的理论与技术框架.航空学报,2005,26(5):680-686.
    [76]张士峰,蔡洪.小子样条件下可靠性试验信息的融合方法.国防科技大学学报,2004,26(6):25-29.
    [77]冯静.小子样复杂系统可靠性信息融合方法与应用研究.国防科学技术大学博士学位论文,2004.
    [78]王华伟.液体火箭发动机可靠性增长分析与决策研究.宇航学报,2004,06:63-66.
    [79]Elperin T.,Gertsbakh I.,Bayes credibility estimation of an exponential parameter for random censoring and incomplete information.IEEE Transactions on Reliability,1990,39(2):204-208.
    [80]Mazzuchi T.A.,Soyer R.,A Bayes methodology for assessing product reliability during development testing.IEEE Transactions on Reliability,1993,42:503-510.
    [81]Hamada M.,Martza H.F.,Reeseb C.S.,etc.A fully Bayesian approach for combining multilevel failure information in fault tree quantification and optimal follow-on resource allocation.Reliability Engineering & System Safety,2004,86(3):297-305.
    [82]Bayarri M.J.and Berger J.O.,The interplay of Bayesian and frequentist analysis.Statistical Science,2004,19(1):58-80.
    [83]宋保维,严卫生.鱼雷可靠性评定中的Bayes方法.机械科学与技术,1998,17(3):371-374.
    [84]张金槐.Bayes试验分析方法.北京:国防科技大学出版社,2007.
    [85]冯蕴雯,黄玮,吕震宙,宋笔锋.极小子样试验的半经验评估方法.航空学报,2004,25(5):456-459.
    [86]Ramirez-Marquez J.E.,Wei Jiang.On improved confidence bounds for system reliability.IEEE Transactions on Reliability,2006,55(1):26-36.
    [87]Quigley J.,Walls L.,Nonparametric bootstrapping of the reliability function for multiple copies of a repairable item modeled by a birth process.IEEE Transactions on Reliability,2005,54(4):604-611.
    [88]Phuc D.V.,Anne B.,Christophe B.,Reliability importance analysis of Markovian systems at steady state using perturbation analysis.Reliability Engineering and System Safety,2008,20(2):1:11.
    [89]Guo Haitao,Yang Xianhui.Automatic creation of Markov models for reliability assessment of safety instrumented systems.Reliability Engineering and System Safety,2008,93:807-815.
    [90]Cardoso J.B.,Almeida J.R.,Dias J.M.,Coelho P.G.,Structural reliability analysis using Monte Carlo simulation and neural networks.Advances in Engineering Software,2008,39(6):505-513.
    [91]Vapnik V.The nature of statistical learning theory.Springer,Berlin Heidelberg New York,1995.
    [92]Sugumaran V.,Sabareesh G.R.,Ramachandran K.I.,Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine.Expert Systems with Applications,2008,34(4):3090-3098.
    [93]Hong Jin-Hyuk,Min Jun-Ki,Cho Ung-Keun,Cho Sung-Bae.Fingerprint classification using one-vs-all support vector machines dynamically ordered with nai"ve Bayes classifiers.Pattern Recognition,2008,41(2):662-671.
    [94]Lu Wei,Chung Fu-Lai,Lu Hongtao,Choi Kup-Sze.Detecting fake images using watermarks and support vector machines.Computer Standards & Interfaces,2008,30(3):132-136.
    [95]Lau K.W.,Wu Q.H.,Local prediction of non-linear time series using support vector regression.Pattern Recognition,2008,41(5):1539-1547.
    [96]Rocco C.M.and Moreno J.A.,Fast Monte Carlo reliability evaluation using support vector machine.Reliability Engineering and System Safety,2002, 76:237-243.
    [97]Chakguy P.,Theodore B.Trafalis,S.Raman.Support Vector Regression for Determination of Minimum Zone.Transactions of the ASME,2003,125:736-739.
    [98]Hurtado J.E.and Alvarez D.A.Classification approach for reliability analysis with stochastic finite-element modeling.Journal of structural engineering,2003,8:1141-1149.
    [99]Hong Wei-Chiang,Pai Ping-Feng.Predicting engine reliability by support vector machines.Int J Adv Manuf Technol,2005,17(4):2340-2348.
    [100]Pai Ping-Feng.System reliability forecasting by support vector machines with genetic algorithms.Mathematical and Computer Modelling,2006,43:262-274.
    [101]李洪双,吕震宙.支持向量回归机在结构可靠性分析中的应用.航空学报,2007,28(1):94-99.
    [102]马超,吕震宙.结构可靠性分析的支持向量机分类迭代算法.中国机械工程,2007,18(7):816-819.
    [103]王爱玲,白恩远,赵学良.现代数控机床.北京:国防工业出版社,2003.
    [104]师汉民.金属切削理论及其理论探索.武汉:华中科技大学出版社,2003.
    [105]杨棣,唐恒龄,廖伯瑜.机床动力学(Ⅱ).北京:机械工业出版社,1983.
    [106]杨叔子,吴雅.时间序列分析的工程应用(上册).武汉:华中理工大学出版社,1991.
    [107]Pisarn C.,Theeramunkong T.,An HMM-based method for Thai spelling speech recognition.Computers & Mathematics with Applications,2007,54(1):76-95.
    [108]Thomas P.,Gernot A.Fink.Pattern recognition methods for advanced stochastic protein sequence analysis using HMMs.Pattern Recognition,2006,39(12):2267-2280
    [109]Benyoussef L.,Carincotte C.,Derrode S.,Extension of higher-order HMC modeling with application to image segmentation.Digital Signal Processing,2008,18(5):849-860.
    [110]Zhu K.,Wong Y.S.,Hong G.S.,Multi-category micro-milling tool wear monitoring with continuous hidden Markov models,Mechanical Systems and Signal,2008.
    [111]龚光鲁,钱敏平.应用随机过程教程及其在算法与智能计算中的应用.北京:清华大学出版社,2003
    [112]Vladimir N.Vapnick.Statistical learning theory.John Wiley & Sons Inc.,1998.
    [113]Smola A.J.and Sch(o|¨)lkopf B.,A tutorial on support vector regression.NeuroCOLT2 Technical Report Series,NC2-TR-1998-030,Berlin,Germany.
    [114]Stella M.Clarke,Jan H.Griebsch,Timothy W.Simpson.Analysis of support vector regression for approximation of complex engineering analyses.Journal of Mechanical Design,2005,127:1076-1087.
    [115]肖刚,李天柁著.系统可靠性分析中的蒙特卡罗方法.北京:科学出版社,2003.3
    [116]Kijewski T.,Kareem A.,On the reliability of a class of system identification techniques:insights from bootstrap theory.Structural Safety,2002,24(2-4):261-280.
    [117]Heiermann K.,Riesch-Oppermann H.,Huber N.,Reliability confidence intervals for ceramic components as obtained from bootstrap methods and neural networks.Computational Materials Science,2005,34(1):1-13.
    [118]周勇,陈吉红,彭芳瑜.高速高精度数控进给驱动的机电联合系统仿真.机械科学与技术,2007,26(2):135-139.
    [119]周勇.高速进给伺服系统动态特性分析及其运动控制研究.华中科技大学博士学位论文,2008.
    [120]何贡.互换性与测量技术(第二版).北京:中国计量出版社,2005.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700