带单个变点AR(1)模型的统计推断

变点时间序列一直是计量经济学、工程学和统计学的一个重要研究课题,在金融、气象和工业等领域有着广泛的应用。研究了带单个变点一阶自回归(AR(1))模型的统计推断问题。基于极大似然(或拟似然)方法,针对带单个变点AR(1)模型给出了参数估计表达式及自相关系数估计的一致性条件,同时得到了该条件下自相关系数极大似然(或拟似然)估计的渐近分布,并依此讨论了模型是否存在变点的假设检验及自相关系数变化增量的假设检验问题。最后通过数值模拟和上证综合指数日交易量的实证分析说明了所提理论和方法的有效性。     

自相关过程的修正均值控制图在MAP中的应用

传统控制图的应用前提是来自过程的观测值彼此独立,但通过自动化测试设备获得的校准值通常存在自相关现象,违背独立性假定.结合新型计量保证方案应用的工程实际,对观测值的自相关参数做出辨识和估计,建立一阶自回归时间序列模型,修正了传统控制图的控制限,使之适用于自相关过程.实例分析表明,当过程观测值存在自相关时使用常规控制图将得到错误的控制限设置,使用修正均值控制图则可以正确判断过程是否受控.     

基于ARMA(n,n-1)模型的统计质量控制图

将ARMA(n,n-1)模型应用到具有自相关性的生产过程中,提出了基于该模型的统计控制图.使用蒙特卡洛方法进行数据模拟,与其它文献比较可得,当生产过程平稳时,ARMA(n,n-1)控制图的性能要远远地超过ARMA(1,1)控制图;当生产过程不平稳时,ARMA(n,n-1)控制图也能达到预期目的.并在此基础上,设计了AR...     

雨刮电动机精磨主轴外圆系统过程能力的动态研究

通过对实际有偏移的过程能力指数涵义的诠释,提出不能仅根据Cpk值的大小来评价制造系统的过程能力,应该将Cpk与Cp和Ca综合使用,才能更全面、更深刻地了解制造系统的过程能力的观点.用1-AGO方法对桑塔纳雨刮电动机精磨主轴外圆系统前30个月测定的Cp和Ca值的波动规律进行了白化,并分别对其建立了灰色动态模型.经对模型的预测准确性检验,Cp和Ca动态模型的还原值与实测值最大相对误差分别为-3.6673%和-2.4127%,利用建立的模型对Cp和Ca未来的波动趋势进行预测,为过程控制人员的决策提供了宝贵的数据.     

基于AR双谱分析的电梯运行质量研究

文中通过采集电梯运行过程中轿厢的加速度振动信号,建立时间序列的自回归功率谱和双谱模型。结果表明,通过双谱幅值的分析、自相关功率谱与重构功率谱的比较,可以获得信号的非高斯、非线性频率特性信息,对分析电梯运行质量具有重要的指导意义。     

度量工业过程能力的重要指标C_(PK)

讨论了基本的过程能力指数之一CPK的重要作用及与其它指数之间的关系,以及确定的方法;最后说明CPK在使用时应该注意的问题.     

过程能力指数和性能指数的若干问题研究

通过对过程能力指数Cpk和过程性能指数Ppk的定义及计算过程的研究,分析Cpk和Ppk的不同,对常用的过程能力指数做出了较为全面的解释,澄清了“Cpk为零就是合格品率为零的”错误观点,并且给出了两种指数的使用范围.     

灰色模型GM(1, 1)优化

分析了GM(1, 1)模型产生模拟误差的原因，经大量的数据模拟和GM(1, 1)模型比较，发现背景值的优化使GM(1, 1)模型在短期、中期及长期预测中扩大了适用范围，并且模拟及预测精度显著提高。     

神经网络方法在自相关过程控制中的应用

将传统休哈特控制图应用于自相关过程控制时,会引发大量虚发报警.本文将使用时间序列模型模拟自相关过程并将神经网络方法引入自相关过程控制中.以神经网络特有的模式识别技术,对自相关过程中均值发生突变的情况进行监控,取得了良好效果.     

Li_(1+x)Ti_(2-x)Cr_xP_3O_(12)系统的相关系与电性

研究了 Li_(1+x)Ti_(2-x)Cr_xP_3O_(12)系统的相组成与电性的关系。结果表明,Cr~(3+)能取代 LiTi_2P_3O_(12)中的Ti~(4+)生成固溶体,直至 x=0.8。在固溶体范围内,电导率随 x 的增加不断升高,至 x=0.8时达到极大。此时在室温和300℃的电导率分别为2.4×10~(-4)和4.8×10~(-2)S/cm,电子迁移数在10~(-4)数量级,分解电压为1.8V。对所得结果从结晶化学角度进行了讨论。     

The New Nino Capability Index for Dynamic Process Capability Analysis

The process capability analysis is a crucial activity to evaluate if the process outcome meets the design specifications. Classically, such analysis is performed by verifying the in‐control condition of the process and evaluating suitable capability indices, by assuming the process in‐control steady‐state condition. However, the in‐control period of the process characterizes only a part of the system functioning cycle, the one with the lower defective rate. In particular, the system functioning cycle is also characterized by the out‐of‐control period, during which a greater defective rate is produced, and such increasing is not considered by the widely adopted capability indices. As consequence, the classical approaches to perform the process capability analysis involves an overestimation of the process capability level. For this reason, in order to overcome the previously described limitation, in the present paper it is proposed a new capability index based on the real defective rate of the process. Thus, such new index is able to estimate the real process capability level. Finally, in order to compare the new index to the conventional C_p capability index, a numerical comparison study related to a process capability analysis is carried out, and the related practical considerations are given. Copyright © 2013 John Wiley & Sons, Ltd.     

工序能力分析与评价中的几个问题

在工序质量分析与控制中,计算与评价工序能力指数是一项非常重要的工作,也是计算机辅助质量系统的一个重要模块。文章针对目前在工序能力计算与分析中出现的问题,提出了如何合理地进行抽样、样本数据的正态性检验以及对非正态性数据的处理、Ｃｐ的置信区间以及与样本含量的关系,旨在为实际生产过程中质量工程师进行工序能力分析和评价提供指导。     

基于成本控制的小批量生产工序能力指数研究

传统的工序能力指数分析方法仅仅从统计质量管理的角度对过程能力进行分析,而忽略了所加工产品的不同批量、不同价值、不同加工工序成本的影响。对国内外多品种、小批量的工序能力指数研究现状进行了回顾,在对工序能力指数的点估计及置信区间进行分析的基础上,提出了基于成本控制的多品种、小批量生产方式下确定工序能力指数的方法,从而达到降低生产加工过程成本的目的。     

Process capability analysis in the presence of autocorrelation

Process capability analysis when observations are autocorrelated is addressed using time series modelling and regression analysis. Through the use of a numerical example, it is shown that the variance estimate obtained from the original data is no longer an appropriate estimate to be considered for conducting process capability analyses. Copyright © 2002 John Wiley & Sons, Ltd.     

AR（p）模型参数的灰色关联度ARGR诊断方法

本文提出了一种以AR(p)参数构成的二次特征量来计算系统各种状态之间灰色关联度的AR-GR新方法。该法综合了AR(户)模型Marple 算法和灰色关联度GR 算法的优点,可用于工程信号的分类处理和设备的监测诊断。用编制的计算机程序对实测信号进行检验,结果表明。该方法具有快速、准确的特点,有广阔的应用前景。     

One‐sided Process Capability Assessment in the Presence of Measurement Errors

In the manufacturing industry, many product characteristics are of one‐sided specifications. The well‐known process capability indices C_PU and C_PL are often used to measure process performance. Most capability research works have assumed no measurement errors. Unfortunately, such an assumption is not realistic even if the measurement is conducted using highly sophisticated advanced measuring instruments. Therefore, conclusions drawn regarding process capability are not reliable. In this paper, we consider the estimation and testing of C_PU and C_PL with the presence of measurement errors, to obtain adjusted lower confidence bounds and critical values for true process capability, which can be used to determine whether the factory processes meet the capability requirement when the measurement errors are unavoidable. Copyright © 2005 John Wiley & Sons, Ltd.     

On the Implementation of the Principal Component Analysis–Based Approach in Measuring Process Capability

The use of principal component analysis in measuring the capability of a multivariate process is an issue initially considered by Wang and Chen (1998). In this article, we extend their initial idea by proposing new indices that can be used in situations where the specification limits of the multivariate process are unilateral. Moreover, some new indices for multivariate processes are suggested. These indices have been developed so as to take into account the proportion of variance explained by each principal component, thus making the measurement of process capability more effective. Copyright © 2011 John Wiley & Sons, Ltd.     

Multivariate Process Capability Vector Based on One‐Sided Model

To evaluate the capability of manufacturing processes in satisfying the customer's needs, a variety of indices has been developed. Some of them are introduced by researchers to analyse the processes with multivariate quality characteristics. Most of the proposed in the literature multivariate capability indices are defined under assumption of normality distribution of the quality characteristics. Thus, the process region describing the variation of the data has an elliptical shape. In this paper, a multivariate process capability vector with three components is introduced, which allows to access the capability of a process with both normally and non‐normally quality characteristics due to application of a pair of one‐sided models as the process region shape. At the beginning, one‐sided models are defined, next the proposed vector components are proposed and the methodology of their evaluation is presented. The methodology (which in fact could be also applied to both the correlated and non‐correlated characteristics) is verified by applying simulation and real problems. The obtained results show that the proposed methodology performs satisfactorily in all considered cases. Copyright © 2014 John Wiley & Sons, Ltd.