摘要
进入21世纪以来,质量受到了前所未有的重视。统计过程调整作为一种在线质量控制技术,主要通过对过程的控制变量进行调整,来实现在线减小过程的波动,从而提高产品质量,是实施过程改进和保证产品质量所必须的技术之一。对于复杂产品而言,随着产品复杂程度的不断增加,其制造过程呈现少样机甚至无样机化生产、在线调整受影响的因素多、多质量特性等特点,这些都对统计过程调整技术的研究和应用提出了更高的要求。
本论文在已有的统计过程调整技术研究的基础上,以复杂产品离散制造过程的在线质量控制为研究对象,通过对不同类型的过程进行分析和建模,以及对过程输出质量特性进行估计和控制,为复杂产品制造过程的在线质量控制提供技术和方法;结合过程的费用结构,设定过程的调整策略,在保证过程产品质量的同时降低过程的总体质量损失。具体的研究内容包括:
首先,研究了参数未知自相关过程的在线调整问题。在复杂产品的实际生产中,由于少样机甚至无样机化生产使得过程参数往往很难精确知道。基于此,通过对目标过程建立状态空间方程模型,并利用贝叶斯方法估计过程的未知参数,考虑调整不需要成本和每次调整的成本恒定的情形,基于序贯蒙特卡洛法分别给出了两种情形下使过程总体损失最小的在线调整策略。
其次,针对过程的调整存在随机误差,而且很多情形复杂产品生产的批次比较少、设置偏差对过程输出质量影响尤为突出的实际,对考虑调整随机误差的设置调整问题进行了分析。考虑到每次调整需要成本,通过对过程输出质量特性偏离目标值引起的损失和调整成本之间进行权衡,基于过程输出质量特性的预报分布,设计了过程相应的边界形式的调整策略。在该调整策略中,调整边界是随生产阶段而变化的,并且由于调整的随机误差会影响过程波动,每个阶段的调整边界还与过程波动的估计相关。仿真分析结果验证了该调整策略的可行性与有效性。
最后,考虑到许多复杂产品最终产品的质量往往需要多个质量特性值来进行刻画,本论文对多变量的统计过程调整问题进行了研究。对过程参数已知和未知两种情形进行分析,由于调整需要成本,需要在过程输出质量特性偏离目标引起的损失和调整成本之间进行权衡,给出了两种情形下的调整策略。在过程参数已知的情形中,推导了该情形下边界调整策略后,还分析了过程参数、调整费用等对调整边界和调整效果的影响。在过程参数未知的情形中,通过分析发现过程输出质量特性向量的分布与过程参数已知情形下的分布完全不同,给出了根据下一阶段输出质量特性预报分布的边界形式的调整策略,并分析了过程生产阶段,先验信息等对调整效果的影响。通过与其它调整策略进行比较的仿真分析结果来看,两种情形下的调整策略都能更为有效地减少过程的质量损失。
本论文通过对不同过程模型以及费用结构下的过程调整策略进行研究,分别实现了对复杂产品制造过程中参数未知、调整存在随机误差、多质量特性等情形下的在线质量控制。本论文的研究是对统计过程调整理论的丰富,并为复杂产品制造过程提高质量,降低成本提供了有效的指导。
Quality has been given attention more than ever before since 21st century. Statistical process adjustment is one kind of online quality control technologies that can be used to reduce the process variation online to improve the quality of products, mainly by adjusting the level of control factors. It is therefore also one of the necessary technologies of process improvement and quality assurance. With the increasing degree of complexity of products, the manufacturing processes of complex products (system) have shown many features that raise higher requirements for the research and application of statistical process adjustment, such as no pilot or little pilot study, online adjustment easily influenced by so many factors, multiple quality characteristics, and so on.
Based on existing research of statistical process adjustment, more embedded study of the online quality control of the discrete manufacturing process of complex products (system) is conducted and presented. Technologies and methods are provided through the modelling and analysis of different kind of processes, and the estimation, control of the characteristics of process output quality. Cost structure is taken into account in setting up the adjustment strategies in order to reduce the overall cost and ensure the quality of the manufacturing process of complex products (system) at the same time.
This research can be summarized as follows:
Firstly, the adjustment problem for autocorrelation process with unknown parameters is discussed. In practice, since there is very little data from pilot study, the process parameters are too difficult to obtain. By building the state space model for the process, and estimating the unknown parameters by using Bayesian method, given the situations of the adjustment that has a cost or not, the adjustment strategy to minimize the total process lost based on sequencial Monte Carlo method is provided.
Secondly, the setup adjustment problem with adjustment error is discussed given that in the manufacturing of complex products (system), short-run processes are becoming more prevalent due to high-degree customization, the adjustment of the process with initial offset is very important, and the adjustment action may also have random error. Since the adjustment has a cost, we trade off the loss caused by the deviation from target of the quality characteristic and the adjustment cost. Based on the deduced predictive distribution of the quality characteristic, the deadband form adjustment strategy is proposed. In this adjustment strategy, the adjustment limits are time-varying, and depending on the stage of process and the process variance. Simulation results demonstrate the feasibility and effectiveness of the proposed adjustment strategy.
Lastly, considering that the quality of end products needs to be described by several characteristics in many manufacturing processes of complex products (system), the multivariate statistical process adjustment problem is discussed. We analyze the problem under the known or unknown process parameters respectively. Given fixed adjustment cost and the loss caused by the deviation from target to the quality characteristics, the adjustment strategy is presented respectively. In the situation of known process parameters, the sensitivity of the adjustment strategy with respect to the process parameters and adjustment cost are investigated. In the situation of unknown process parameters, the sensitivity of the adjustment strategy with respect to the number of process stage and prior information are also investigated. With comparison studies, it could be seen that both presented adjustment strategies can achieve good adjusment performance as well as reduce lost effectively in the process.
The adjustment strategies for different processes and with different cost structure proposed in the thesis are adaptable to multiple conditions, such as process with unknown parameters, adjustment with random error, and multiple quality characteristics. This research enriches the theory of the statistical process adjustment, and provides guidance to enhance the quality of products, minimize quality loss in the manufacturing of complex products (system).
引文
[1]Box G E P, Krammer T. Statistical process monitoring and feedback adjustment:A discussion[J]. Technometrics,1992,34(3):251-267
[2]Del Castillo E. Statistical process adjustment for quality control[M]. New York: John Wiley & Sons,2002
[3]乐清洪,腾霖,朱名铨,王润孝.先进制造环境下质量控制的理论与方法[J].航空精密制造技术,2004,40(2):1-5
[4]许静,祁国宁,杨青海,顾新建,刘曦泽.面向大批量定制的质量控制研究与实现[J].浙江大学学报(工学版),2009,43(12):2293-2299
[5]Hamsen K L, Rush H. Hotpots in complex product systems:emerging issues in innovation management[J]. Technovation,1998,8(8):555-561
[6]李伯虎.复杂产品制造信息化的重要技术[J].中国制造业信息化,2006,14(7):19-23
[7]Montgomery D C. SPC research-current trends[J]. Quality and Reliability Engineering International,2007,23 (5):515-516
[8]戴明著,钟汉清,戴久永译.戴明论质量管理:以全新视野来解决组织及企业的顽症[M].海口:海南出版社,2003
[9]Macgregor J F. A different view of the funel experiment[J]. Journal of Quality Technology,1990,22(4):255-259
[10]Davis W. Using corrective action to make matters worse by not considering vriation[J], Quality Progress,2000,33(10):56-61
[11]Box G E P, Jenkins G M. Some statistical aspects of adaptive optimization and control[J]. Journal of the Royal Statistical Society B,1962,24(2):297-343
[12]Box G E P, Jenkins G M. Further contributions to adaptive quality control: simultaneous estimation of dynamics:no-zero cost[J]. Bulletin of International Statistical Institute,1963,34:943-974
[13]Grant E L, Leavenworth R S.统计质量控制[M].北京:清华大学出版社,2002
[14]Montgomery D C. Introduction to statisitcal process control(the fifth edition)[M]. New York:John Wiley & Sons,2005
[15]Hunter J S. The exponentially weighted moving average[J]. Journal of Quality Technology,1986,18 (4):203-210
[16]Box G E P, Coleman D E, Baxley R V. A comparison of statistical process control and engineering process control[J] Journal of Quality Technology,1997,29(2): 128-130
[17]Horel R W et. A Discussion:Integrating SPC and APC[J]. Technometrics,1992, 34 (3):268-285
[18]Box G E P, Luceno A. Discrete proportional-integral adjustment and statistical process control[J]. Journal of Quality Technology,1997,29(3):248-260
[19]Box G E P, Luceno A. Statistical Control by Monitoring and Feedback Adjustment[M]. New York:John Wiley & Sonc,1997
[20]Montegomery D C, Keats J B, Runger G C, Messina W S. Integrating statistical process control and engineering process control[J]. Journal of Quality Technology, 1994,26 (2):79-87
[21]Montegomery D C, Keats J B, Yatskievitch M, Messina W S. Integrating statistical process monitoring with feedforward control [J]. Quality and Reliability Engineering International,2000,16 (6):515-520
[22]Del Castillo E. A note on two process adjustment models[J]. Quality and Reliability Engineering International,1998,14 (1):23-28
[23]Del Castillo E. Statistical process adjustment:a brief retropective, current status, and some opptortunities for future work[J]. Statistica Neerlandica,2006,60(3): 309-326
[24]Janakiram M, Keats J B. Combining SPC and EPC in a hybrid industry [J]. Journal of Quality Technology,1998,30 (3):189-200
[25]Shi J J, Zhou S Y. Quality control and improvement for multistage systems:A survey[J]. IIE Transactions,2009,41 (9):744-753
[26]Del Castillo E, Hurwitz A M. Run-by-Run process control:literature review and exensions[J]. Journal of Quality Technology,1997,29 (2):184-196
[27]Jin M, Tsung F. Smith-EWMA run-to run control schemes for a process with measurement delay[J]. IIE Transactions,2009,41 (4):346-358
[28]Joseph V, Wu C F J. Robust parameter design of multiple target systems[J]. Technometrics,2002,44(4):338-346
[29]Mannar K, Ceglarek D. Functional capability space and optimum process adjustments for manufacturing processes with in specs failure[J]. IIE Transactions, 2010,42(2):95-106
[30]Izquierdo L E, Shi J J, Hu S J, Wampler C W. Feedforward control of multistage assembly processes using programmable tooling[J]. Transactions of NAMRI/SME, 2007,35:295-303
[31]He F, Wang K, Jiang W. A general harmonic rule controller for Run-to-Run process control[J]. IEEE Transactions on Semiconductor Manufacturing,2009, 22(2):232-244
[32]Tseng S, T, Jou B Y, Liao C H. Adaptive variable EWMA controller for drifted processes [J]. IIE Transactions,2010,42(4):247-259
[33]Chen A, Guo R. Aged-based double EWMA controller and its application to CMP processes[J]. IEEE Transactions on Semiconductor Manufacturing,2001,14 (1): 11-19
[34]Patel N S, Jenkins S T. Adaptive optimization of run-to run controllers:The EWMA exaple[J]. Transactions on Semiconductor Manufacturing,2000,13 (1): 97-107
[35]Tseng S T, Yeh A B, Tsung F, Chan Y Y. A study of variable EWMA controller [J]. Transactions on Semiconductor Manufacturing,2003,16 (4): 633-643
[36]Sachs E, Hu A, Ingolfsson A. Run by run process control:Combining SPC and feedback control[J]. Transactions on Semiconductor Manufacturing,1995,8(1): 26-43
[37]Robbins H, Monro S. A stochstic approximation method[J]. Annals of Mathematical Statistics,1951,22(3):400-407.
[38]Box G E P, Tiao G C. Bayesian inference in statistical analysis[M]. New York: John Wiley & Sons,1992
[39]Kotz S,吴喜之.现代贝叶斯统计学[M].北京:中国统计出版社,2000
[40]Ho R K W, Hu I. Flexible modeling of random effects in linear mixed models- a Bayesian approach[J]. Computational Statistics and Data Analysis,2008,52 (3): 1347-1361
[41]Marcellus R L. Bayesian monitoring to detect a shift in process mean[J]. Quality and Reliability Engineering International,2008,24 (3):303-313
[42]Marcellus R L. Bayesian statistical process control[J]. Quality Engineering, 2008,20 (1):113-117
[43]Triantafyllopoulos K. Multivariate control charts based on Bayesian state space models[J]. Quality and Reliability Engineering International,2006,22 (6) 693-707
[44]Tsiamyrtzis P, Hawkins D M. A Bayesian scheme to detect changes in the mean of a short-run process[J]. Technometrics,2005,47(4):446-456
[45]Bayarri M J, Garcia-Donato G. A Bayesian sequential look at u-control charts[J]. Technometrics,2005,47(2):142-151
[46]Kooli I, Limam M. Bayesian np control charts with adaptive sample size for finite production runs[J]. Quality and Reliability Engineering International,2009, 25 (4):439-448
[47]Colosimo B M, Del Castillo E. Bayesian process monitoring, control and optimization[M]. New York; Chapman & Hall/CRC,2007
[48]Rue H, Hum M A. Bayesian object identification[J]. Biometrika,1999,86(3): 649-660
[49]Chen J H, Kuo T W, Lee A C. Run-by-run process control of metal sputter deposition:Combining time series and extended kalman filter [J]. Transactions on Semiconductor Manufacturing,2007,20 (3):278-285
[50]Box G E P, Jenkins G M, Reinsel G. Time series analysis, forecasting, and control[M]. New Jerey:Prentice-Hall,1994
[51]Pan R. Statistical process adjustment methods for quality control in short-run manufacturing[D]. Pennsylvania:Pennsylvania State University,2002
[52]Wurl R C, Albin S L, Shiffer I J. Multivariate monitoring of batch process startup[J]. Quality and Reliability Engineering International, 2001,17(4):269-278
[53]Xu D, Albin S L. Manufacturing start-up problem solved by mixed-integer quadratic programming and multivariate statistical modelling [J] International Journal of Production Research,2002,40 (3):625-640
[54]Nembhard H B, Birge J R. A startup procedure for process industries using a multiple objective nonlinear program [J]. IIE Transactions,1998,30 (4):291-300
[55]Nembhard HB, Mastrangelo C M, Kao S M. Statistical monitoring performance for startup operations in a feedback control system[J]. Quality and Reliability Engineering International,2001,17 (5):379-390
[56]Nembhard H B, Mastrangelo C M. Integrated process control for startup operations[J]. Journal of Quality Technology,1998,30 (3):201-211
[57]格里纳著,何桢译.质量策划与分析[M].北京:中国人民大学出版社,2005
[58]Majeske K D, Hammett P C. Identifying sources of variation in sheet metal stamping[J]. The International Journal of Flexible Manufacturing Systems,2003,15 (1):5-18
[59]Grubbs F E. An optimum procedure for setting machines or adjusting processes[J]. Industrial Quality Control, July 1954, reprinted in Journal of Quality Technology, 1983,15(4):186-189
[60]Trietsch D. The harmonic rule for process setup adjustment with quadratic loss[J]. Journal of Quality Technology,1998,30(1):75-84
[61]Trietsch D. Process setup adjustment with quadratic loss[J]. HE Transactions, 2000,32(4):299-307
[62]Del Castillo E, Pan R, Colosimo B M. A unifying view of some process adjustment method[J]. Journal of Quality Technology,2003,35(3):286-293
[63]Del Castillo E, Pan R, Colosimo B M. Small sample performance of some statistical adjustment methods[J]. Communications in Statistics-Simulation and Computation,2003,32(3):923-942
[64]Colosimo B M, Pan R, Del Castillo E. Setup adjustment for discrete-part manufacturing processes with asymmetric cost functions [J]. International Journal of Production Research,2005,43(18):3837-3854
[65]Lian Z, Del Castillo E. Setup adjustment under unknown process parameters and fixed adjustment cost[J]. Journal of Statistical Planning and Inference,2006,136(3): 1039-1060
[66]Lian Z. Bayesian statistical process adjustment for unknown parameter systems[D]. Pennsylvania:Pennsylvania State University.2005
[67]Lian Z, Colosimo B M, Del Castillo E. Setup adjustment of multiple lots using a sequential Monte Carlo method[J]. Technometrics,2006,48(3):373-385
[68]Lian Z, Colosimo B M, Del Castillo E. Setup error adjustment:Sensitivity analysis and a new MCMC control rule[J]. Quality and Reliability Engineering International, 2006,22 (4):403-418
[69]Luceno A. Performance of discrete feedback adjustment schemes with deadband, under stationary bersus nonstationary stochastic disturbance[J]. Technometrics, 1998,40 (3):223-233
[70]Guo R S, Chen A, Chen J J. An enhanced EWMA controller for processes subject to random disturbances. Run to Run process control in semiconductor manufacturing[M]. Edited by Monye, Boca Raton:CRC Press,2000
[71]Chen A, Elsayed E A. An alternative mean estinator for processes monitored by SPC charts[J]. International Journal of Production Research,2000,38(12): 3093-3109
[72]Nembhard H B, Nembhard A. The use of Bayesian forecasting to make process adjustments during transitions[J]. European Journal of Operational Research,2001, 130 (2):437-448
[73]Crowder S V, Eshleman L. Small sample properties of an adaptive filter applied to low volume SPC[J]. Journal of quality Technology,2001,33(1):29-38
[74]褚崴,于晓义,孙树栋.基于马尔可夫链蒙特卡洛法的统计过程调整技术研究[J].计算机集成制造系统,2007,13(6):1210-1217
[75]Apley D W, Kim J. Cautious control of industrial process variability with uncertain input and disturbance model parameters [J]. Technometrics,2004,46(2): 188-199
[76]Apley D W. A cautious minimum variance controller with ARIMA disturbances [J]. HE Transactions,2004,36 (5):417-432
[77]Shi J J, Apley D W. A suboptimal N-step-ahead cautious controller for adaptive control applications [J]. Journal of Dynamic Systems, Measurement, and Control, 1998,120:419-423
[78]Shi J J, Apley D W. An adaptive cautious predictive controller for real-time implementation[J]. Dynamic systems and control,1994,55(1):167-174
[79]Tseng S T, Lin C H. Stablity analysis of single EWMA controller under dynamic models[J]. HE Transactions,2009,41 (7):654-663
[80]Ingolfsson A, Sachs E. Stability and sensitivity of an EWMA controller [J]. Journal of Quality Technology,1993,25(4):271-287
[81]Tseng S T, Chou R J, Lee S P. Statistical design of double EWMA controller [J]. Applied Stochastic Models in Business and Industry,2002,18 (3):313-322
[82]Tseng S T, Hsu N J. Sample-size determination for achieving asymptotic stability of a double EWMA control scheme[J]. IEEE Transactions on Semiconductor Manufacturing,2005,18 (1):104-111
[83]Tseng S T, Tang J, Lin C H. Sample-size determination for achieving stability of double multivariate exponentially weighted moving average controller [J]. Technometrics,2007,49 (4):409-419
[84]叶亮,潘尔顺,奚立峰.基于实验设计的健壮参数控制方法[J].上海交通大学学报,2006,40(6):918-921
[85]叶亮,潘尔顺,奚立峰.模型精度对自控性能的影响及谨慎调整策略[J].计算机集成制造系统,2007,13(10):1952-1955
[86]Dasgupta T, Wu C F J. Robust parameter design with feedback control[J]. Technometrics,2006,48(3):349-360
[87]Jin J H, Shi J J. Shut height adjustment based on the feedforward control of the noise factor estimation information in sheet metal stamping[R]. IPQI Technical report,1999
[88]Jin J H, Ding Y. Online automatic process control using observable noise factors for discrete-part manufacturing[J]. HE Transactions,2004,36 (9):899-911
[89]Joseph V R, Wu C F J. Robust parameter design of multiple target systems[J]. Technometrics,2002,44(4):338-346
[90]Joseph V R. Robust parameter design with feed-forward control [J]. Technometrics, 2003,45(4):284-292
[91]Vanli O A, Del Castillo E. Bayesian approaches for online robust parameter design[J]. HE Transactions,2009,41(4):359-371
[92]Pan R, Del Castillo E. Integration of sequential process adjustment and process monitoring technique[J]. Quality and Reliability Engineering International,2003,19 (4):371-386
[93]Ruhhal N H, Runger G C, Dumitrescu M. Control charts and feedback adjustments for a jump disturbance model[J]. Journal of Quality Technology,2000, 32(4):379-394
[94]Runger G, Testik M C, Tsung F. Relationships among control charts used with feedback control[J]. Quality and Reliability Engineering International,2006,22 (8): 877-887
[95]Sachs E, Hu A. Run by run process control:combining SPC and feedback control [J]. IEEE Transactions on Semiconductor Manufacturing,19958(1):26-43
[96]Tsung F. Improving automatic-controlled process quality using adaptive principal conponent monitoring [J]. Quality and Reliability Engineering International,1999, 15 (2):135-142
[97]Tsung F, Tsui K L. A mean-shift pattern study on the integration of SPC and APC for process monitoring [J]. HE Transactions,2003,35 (3):231-242
[98]Wang K, Tsung F. An adaptive dimension reduction scheme for monitoring feedback-controlled processes[J]. Quality and Reliability Engineering International, 2009,25 (3):283-298
[99]Marion R R, Park C. CUSUM charts for detecting special causes in ingegrated process control[J]. Quality and Reliability Engineering International,2010,26(3): 199-221
[100]王海宇.基于两种反馈控制的过程质量监控[J].航空制造技术,2007,50(5): 77-79
[101]王海宇,徐济超,杨剑锋.基于MMSE和PI控制器的过程质量控制[J].系统工程理论与实践,2006,26(6):75-79
[102]Al-Oraini H A, Rahim M A. Economic statistical design of X control charts for systems with gamma (lamda,2) in-control times [J]. Computers and Industrial Engineering,2002,43 (3):645-654
[103]Nenes G, Tagaras G. An economic comparison of CUSUM and Shewhart charts[J]. IIE Transactions,2008,40 (2):133-146
[104]Nenes G, Tagaras G. The economically designed CUSUM chart for monitoring short production runs[J]. International Journal of Production Research,2006,44 (8): 1569-1587
[105]Nenes G, Tagaras G. The economically designed two-sided Bayesian X-bar control chart[J]. European Journal of Operational Research,2007,183 (1):263-277
[106]Duffuaa S O, Khursheed S N, Noman S M. Integrating statistical process control, engineering process control and Taguchi's engineering[J]. International Journal of Production Research,2004,42(19):4109-4118
[107]Ishikawa K. What is total quality control? [M]. New Jersey:Prentice hall,1985
[108]Kramer T T. Process control from an economic point of view[D]. Wisconsin-Madison:University of Wisconsin-Madison,1989
[109]Taguchi G, Elayed E A, Hsiang T. Quality engineering in production system[M]. New York:McGraw-Hill,1989
[110]Zhang J, Craigmile P F, Cressie N. Loss function approaches to predict a spatial quantile and its exceedance region[J]. Technometrics,2008,50 (2):216-227
[111]Ma Y Z, Zhao F. An improved multivariate loss function approach to optimization[J]. Journal of Systems Science and System Engineering,2004,13(3): 318-325
[112]Ames A E, MacDonald S, Mattucci., Szonyi G, Hawkins D M. Quality loss functions for optimization across multiple response surfaces[J]. Journal of Quality Technology,1997,29(3):339-346
[113]Leon N A. A pragmatic approach to multi-response problems using loss functions[J]. Quality Engineering,1997 9(2):213-220
[114]Suhr R, Batson R G. Constrained multivariate loss function minimization[J]. Quality Engineering,2001,13(3):475-483
[115]Bhamare S S, Yadav O P, Rathore A. A hybrid quality loss function-based multi-objective design optimization approach[J]. Quality Engineering,2009,21(3): 277-289
[116]Feng Q M, Kapur K C. Economic development of specifications for 100% inspection based on asymmetric quality loss functions[J]. IIE Transactions,2006,38 (8):659-669
[117]Sullo O, Vandeven M. Optimal adjustment strategies for a process with run-to-run variation and 0-1 quality loss[J]. IIE Transactions,1999,31 (12): 1135-1146
[118]Harris T J. Optimal controllers for nonsymmetric and nonquadratic loss functions[J]. Technometrics,1992,34(3):298-306
[119]褚崴,于晓义,孙树栋.非均衡失效成本条件下的统计过程调整技术研究[J].计算机集成制造系统,2008,14(6):1168-1175
[120]Box G E P, Luceno A. Discrete proportional-integral control with constraned adjustment[J]. The Statistician,1995,44 (4):479-495
[121]Pan R, Del Castillo E. Integration of sequential process adjustment and process monitoring techniques[J]. Quality and Reliability Engineering International,2003, 19 (4):371-386
[122]Vanli O A, Del Castillo E. Closed-loop system identification for small samples with constraints [J]. Technometrics,2007,49(4):382-394
[123]Del Castillo E. Closed-loop disturbance identification and controller tuning for discrete manufacuturing processes[J]. Technometrics,2002,44(2):134-141
[124]Crowder S V. An SPC model for short production runs:Minimizing expected cost. [J]. Technometrics,1992,34(1):64-73
[125]Box G E P. Feedback control by manual adjustment[J]. Quality Engineering, 1991,4 (1):143-151
[126]Box G E P. Bounded adjustment charts[J]. Quality Engineering,1991,4: 333-340
[127]Box G E P. Process adjustment and quality control[J]. Total Quality Management, 1993,4 (2):215-227
[128]Jensen K L, Vardeman S B. Optimal adjustment in the presence of deterministic process drift and random adjustment error[J]. Technometrics,1993,35 (4):376-389
[129]Box G E P, Luceno A. Selection of sampling interval and action limit for discrete feedback adjustment[J]. Technometrics,1994,36 (4):369-378
[130]Luceno A, Gonzalez F J. Effects of dynamics on the properties of feedback adjustment schemes with dead band[J]. Technometrics,1999,41(2):142-152
[131]Luceno A, Gonzalez F J, Puig-Pey J. Computing optimal adjustment schemes for the general tool-wear problem[J]. Journal of Statistical Computation and Simulation,1996,54 (1):87-113
[132]Luceno A. Minimum cost dead band adjustment schemes under tool-wear effects and delayed dynamics[J]. Statistics and Probability Letters,2000,50 (2):165-178
[133]Luceno A. Effects of adjustment errors on discrete feed-back dead band control schemes[J]. The Statistician,2001,50(2):169-177
[134]Luceno A. Selection of sample size for discrete feedback dead-band control schemes[J]. Communications in Statistics- Theory and Methods,2001,31(4): 679-689
[135]Luceno A. Dead-band adjustment schemes for on-line Feedback quality control[J]. Handbook of Statistics,2003,22:695-727
[136]Box G E P, Luceno A. Discrete proportional-integral adjustment and statistical process control [J]. Journal of Quality Technology,1997,29 (3):248-260
[137]Luceno A. Choosing the EWMA parameter in engineering process control[J]. Journal of Quality Technology,1995,27 (2):162-168
[138]Chou S, Wang M C. Setting policy for on-line process control with dynamic characteristics[J]. Communications in Statistics- Simulation and Computation,2007, 36 (1):217-232
[139]Waner H, Whitin T M. Dynamic version of the economoc lot size model[J]. Management Science,1958,5(1):89-96
[140]Pan R, Del Castillo E. Scheduling methods for the statistical setup adjustment problem[J]. International Journal of Productions Research,2003,41 (7):1467-1481
[141]Lian Z, Del Castillo E. Adaptive deadband control of a drifting process with unknown parameters [J]. Statistics and Probability Letters,2007,77 (8):843-852
[142]Venkatesan G. Cost modeling for feedback control adjustment[J]. International Journal of Production Research,2003,41(17):4099-4114
[143]Jiang W, Tsui K L. An ecomomic model for integrated APC and SPC control charts[J]. HE Transactions,2000,32 (6):505-513
[144]Ling Y, Sheu S H. Economic design of the integrated multivariate EPC and multivariate SPC charts[J]. Quality and Reliability Engineering International,2007, 23 (2):203-218
[145]Robet C, Casella G. Monte Carlo statistical methods(2nd edition)[M]. New York: Spring-Verlag,2004
[146]Hastings W K. Monte Carlo sampling methods using Markov Chains and their applications[J]. Biometika,1970,57 (1):97-109
[147]Besag J. Markov chain Monte carlo methods for statistical inference[R]. Http://www.ims.nus.edu.sg/Programs/mcmc/files/besag_tl.pdf,2004
[148]Scollnic D P M. An introdiction to markov chain monte carlo methods and their actuarial applications[J]. Proceedings of the Casualty Actuarial Society,1996, (83): 114-165
[149]Liu J S. Monte Carlo strategies in scientific computing[M]. New York:Springer, 2008
[150]Jean-Rene L, Janes P R. Reversible jump MCMC for joint detection and estimation of sources in colored noise[J]. IEEE Transactions on Signal processing, 2002,50(2):231-240
[151]Doucet A, Freitas N, Gordon N. Sequential Monte Carlo methods in practice[M]. New York:Springer,2001
[152]Cappe O, Godsill S J, Moulines E. An overview of existing methods and recent advances in sequential Monte Carlo[J]. Proceedings of IEEE,2007,96(5):899-924
[153]Liu J S, Chen R. Sequential Monte Carlo methods for dynamic systems[J]. Journal of the American Statistcial Association,1998,93(443):1032-1044
[154]Doucet A, Godsill S, Andrieu C. On sequential monte carlo sampling methods for Bayesian filtering[J]. Statistics and Computing,2000,10(3):197-208
[155]Smith A F M, Gelfand A E. Bayesuan statistics without tears:a sampling-resampling perspective[J]. The American Statistician,1992,46 (2):84-88
[156]Gordon N J, Salmond D J, Smith A F M. Novel approach to nonlinear/Non-gaussian Bayesian state estimation[J]. IEE Proceedings-F,1993,140 (2):107-113
[157]Geweke. Bayesian inference in economic models using Monte Carlp integration[J]. Econometrion,1989,57(6):1317-1339
[158]Rubin. Using the SIR algorithm to simulate the posterior distributions, in Bayesian Statistics(edited by Bernardo M H, Degroot K M, Lindley D V, Smith A F M)[M]. Oxford University Press,1988,398-402
[159]Kong A, Liu J S, Wong W H. Sequential imputations and Bayesian missing data problems[J]. Journal of the American Statistical Association,1994,89 (5):278-288
[160]Arulampalam S, Maskell S, Gordon N. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J]. IEEE Transactions on Signal Processing,2002,50 (2):174-188
[161]Ridgeway G, Madigan D. A sequential Monte Carlo method for bayesian analysis of massive datasets[J]. Journal of Knowledge Discovery and Data Mining, 2002,7 (3):301-319
[162]Liu J S, Chen R. Blind deconvolution via sequential imputations[J]. Journal of the American Statistical Association,1995,90 (430):567-576
[163]Balakrishnan S, Madigan D. A one-pass sequential Monte Carlo method for Bayesian analysis of massive datasets[J]. Bayesian Analysis,2006,1(2):345-362
[164]马义中.减小和控制多元质量特性波动的理论和方法[D].西北工业大学博士学位论文,西安,2002
[165]Montgomery D C, Mastrangelo C M. Some statistical process control methods for auto-correlated data (with discussion)[J]. Journal of Quality Technology,1991, 23 (3):179-204
[166]Montgomery D C, Mastrangelo C M. SPC with correlated observations for the chemical and process industries [J]. Quality and Reliability Engineering International,1995,11 (2):79-89
[167]Montgomery D C, Woodall W H. A discussion of statistically-based process monitoring and control[J]. Journal of Quality Technology,1997,29 (2):121-162
[168]Box G E P, Narasimhan S. Rethinking statistics for quality control[J]. Quality Engineering,2010,22 (2):60-72
[169]Luceno A. A fast algorithm for the repeated evaluation of the likelihood of a general linear process for long series[J]. Journal of the American Statistical Association,1993,88 (421):229-236
[170]Luceno A. Fast optimization of the exact likelihood of AR and ARMA processes[J]. Computational Statistics and Data Analysis,1994,17 (1):51-63
[171]Luceno A. A fast algorithm for the exact likelihood of stationary and partially nonstationary vector autoregressive-moving average processes[J]. Biometrika,1994, 81 (3):162-168
[172]Luceno A. A fast likelihood approximation for vector general linear processes with long series:application to fractional differencing[J]. Biometrika,1996,83 (3): 603-614
[173]Berger J O, Oliveira V D, Sanso B. Objective Bayesian analysis of spatially correlated data[J]. Journal of the American Statistical Association,2001,96(456): 1361-1374
[174]Qian P Z. Wu C F J. Bayesian hierarchical modeling for integrating low-accuracy and high-accuracy experiments[J]. Technometrics,2008,50(2):192-204
[175]Spiegelhalter D J, Abrams K R, Myles J P. Bayesian approaches to clinical trials and health-care evaluation[M]. West Sussex:John Wiley & Sons,2004.168-174
[176]Knapp R G, Miller M C I. Monitoring simultaneously two or more indices of health care[J]. Evaluation and the Health Professions,1983,6(4):465-482
[177]Gelman A, Carlin J B, Srern H S, Rubin D B. Bayesian data analysis(2rd edition)[M]. New York:Chapman & Hall/CRC,2003
[178]范江华,杨斌妮.多重积分的积分中值定理[J].数学的实践与认识,2007,37(12):197-200