Bayesian Monitoring of Linear Profiles Using DEWMA Control Structures with Random X

The process structures of manufacturing industry are efficiently modeled using linear profiles. Classical and Bayesian set-ups are two well-appreciated schemes for designing control charts for the monitoring of process structures. Mostly in profiles monitoring the independent variables along with the process parameters are assumed fixed. There are manufacturing processes where these conditions may not hold. The advancement in technology and day-to-day changes in process structures caused the parametric uncertainty along with variability in explanatory variables. This paper considered the case of random X and assumes different conjugate and non-conjugate priors to handle parametric uncertainty using double exponentially weighted moving average (DEWMA) control charts. Three univariate DEWMA charts are designed for the monitoring of Y-intercepts, slopes, and error variances. The average run length criterion has been used to evaluate the proposed and competing charts. The wide spread relative study identifies that the proposed Bayesian DEWMA control charts are better than the competing charts based on early detection of out-of-control profiles, particularly for smaller value shifts. The Bayesian DEWMA charts using conjugate priors are the quickest in all as they take less sample points to show out-of-control profile. A case study has been considered to further justify the superiority of Bayesian DEWMA charts over competing charts. 2013 IEEE.The work of S. A. Abbasi was supported by the Qatar University under Project QUST-1-CAS-2018-41.Scopu

The Study on Bayesian Control Charting Structures for Monitoring of Linear Profiles

摘要 统计过程控制（SPC）在制造业质量提升方面做出了许多重要贡献而被广泛认可。那些拥有先进和科学设备的高竞争力制造企业，成功运用SPC来监控他们的产品。这些过程可以用适当的概率分布模型来拟合，从而到达关注和感兴趣的质量特性，同时建立相应的控制图，以确保它的参数稳定性。这些控制图主要基于统计学上两种主要的排列，即古典排列和贝叶斯排列。在古典排列下可以假设感兴趣的参数是固定的。在现代化时代固定参数的假设显得不太实际，在竞争环境中需要不断的调整以适应在市场竞争中新的需求。统计学家们喜欢采用另一种奢华的、更好的、可替代的参数作为随机变量，这种更好的替代就是贝叶斯统计。而这种非确定性参数在量化先前的...Abstract Statistical Process Control (SPC) procedures are widely acknowledged to have made many significant contributions to quality enhancement in the manufacturing industry. Most advanced and scientifically equipped competitive manufacturing companies successfully implement SPC for monitoring their productions. These procedures can model the quality characteristic(s) of interest with an appropr...学位：经济学博士院系专业：经济学院_统计学学号：1542013015440

Univariate and multivariate linear profiles using max type extended exponentially weighted moving average schemes

Many studies have shown that industrial as well as non-industrial business organizations present a growing need of robust and more efficient multivariate monitoring schemes in order to be able to monitor several quality characteristics simultaneous. To monitor two or more parameters simultaneously, several monitoring schemes are used concurrently in most of the cases instead of using a single scheme. Thus, in this paper, the exponentially weighted moving average (EWMA), double EWMA (DEWMA) and the recent triple EWMA (TEWMA) procedures are used to develop new single univariate and multivariate Max-type monitoring schemes for linear profiles under the assumptions of fixed and random linear models to monitor the regression parameters and variance error simultaneously. It is observed that the newly proposed schemes are better alternatives of the classical univariate and multivariate EWMA, DEWMA and TEWMA schemes for linear profiles in terms of the average run-length (ARL) and expected ARL profiles. Numerical examples are presented using simulated and real-life data.https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639Statistic

An Assorted Design for Joint Monitoring of Process Parameters: An Efficient Approach for Fuel Consumption

Due to high fuel consumption, we face the problem of not only the increased cost, but it also affects greenhouse gas emission. This paper presents an assorted approach for monitoring fuel consumption in trucks with the objective to minimize fuel consumption. We propose a control charting structure for joint monitoring of mean and dispersion parameters based on the well-known max approach. The proposed joint assorted chart is evaluated through various performance measures such as average run length, extra quadratic loss, performance comparison index, and relative average run length. The comparison of the proposed chart is carried out with existing control charts, including a combination of X and S, the maximum exponentially weighted moving average (Max-EWMA), combined mixed exponentially weighted moving average-cumulative sum (CMEC), maximum double exponentially weighted average (MDEWMA), and combined mixed double EWMA-CUSUM (CMDEC) charts. The implementation of the proposed chart is presented using real data regarding the monitoring of fuel consumption in trucks. The outcomes revealed that the joint assorted chart is very efficient to detect different kinds of shifts in process behaviors and has superior performance than its competitor charts.Deanship of Scientific Research, King Saud University, King Fahd University of Petroleum and MineralsScopu

An Improved Control Chart for Monitoring Linear Profiles and its Application in Thermal Conductivity

In most of the manufacturing processes, we encounter different quality characteristics of a product and process. These characteristics can be categorized into two kinds; study variables (variable of interest) and the supporting/explanatory variables. Sometime, a linear relationship might exist between the study and supporting variable, which is called simple linear profiles. This study focuses on the simple linear profiles under assorted control charting approach to detect the large, moderate and small disturbances in the process parameters. The evaluation of the proposed assorted method is assessed by using numerous performance measures, for instance, average run length, relative average run length, extra and sequential extra quadratic losses. A comparative analysis of the proposal is also carried out with some existing linear profile methods including Shewhart_3, Hotelling's T{2} , EWMA_3, EWMA/R and CUSUM_3 charts. Finally, a real-life application of the proposed assorted chart is presented to monitor thermal management of diamond-copper composite. 2013 IEEE.This work was supported by the Deanship of Scientific Research (DSR) at King Fahd University of Petroleum and Minerals (KFUPM) under Grant SB191043.Scopu

Run Rules-Based EWMA Charts for Efficient Monitoring of Profile Parameters

In usual quality control methods, the quality of a process or product is evaluated by monitoring one or more quality characteristics using their corresponding distributions. However, when the quality characteristic is defined through the relationship between one or more response and independent variables, the regime is referred to as profiles monitoring. In this article, we improve the performance of the Exponentially Weighted Moving Average Range (EWMAR) control charts, which are implemented for monitoring linear profiles (i.e., intercept, slope and average residual between sample and reference lines) by integrating them with run rules in order to quickly detect various magnitudes of shifts in profile parameters. The validation of the proposed control chart is accomplished by examining its performance using the average run length (ARL) criteria. The proposed EWMAR chart with run rules exhibits a much better performance in detecting small and decreasing shifts than the other competing charts. Finally, an example from multivariate manufacturing industry is employed to illustrate the superiority of the EWMAR chart with run rules. 2013 IEEE.Scopu

Implementation of statistical process control framework with machine learning on waveform profiles with no gold standard reference

[[abstract]]Condensation water temperature profiles are collected from a curing process for high-pressure hose products. The shape of those profiles resembles sine waves with diminishing amplitudes. A gold standard wave profile does not exist. Instead some wave profiles with various frequency and amplitudes are deemed normal for the water release operation. To the best of our knowledge, the current practice and research on SPC do not provide a solution for monitoring wave profiles of this kind. We leveraged existing methods, tools, algorithms that can be found in open source or commercial software for quick response to this type of problem. The proposed SPC implementation framework first converts waveform profiles from the time domain to the frequency domain. Then a set of phase I IX control charts is constructed based on a Partition Around Medoids (PAM) clustering method. A Support Vector Machine (SVM) classifier is then used to label a new profile to its associated group for phase II monitoring so that the IX chart associated with a homogeneous group can provide better process monitoring. Overall 146 water temperature profiles were collected in phase I process, while 39 profiles were captured in phase II process. Out of those 39 profiles, 6 of which were recognized as abnormal waveform profiles by quality engineers and our judgements. The proposed framework with machine learning and SPC implementation in the frequency domain works well during phase I control charting with low false alarm rates. The proposed framework also outperforms the other profile analysis methods in phase II control charting in term of high detection rate of abnormal profiles.[[notice]]補正完

A Bayesian ARMA-GARCH EWMA monitoring scheme for long run : a case study on monitoring the USD/ZAR exchange rate

Statistical process monitoring (SPM) offers an important toolkit used to monitor the stability of a process to improve the quality of outputs and/or services. More often, the design of control charts requires the estimation of the probability density function that involves selecting a common distribution that facilitates the estimation of the process parameters. The Bayesian approach is one of the most efficient techniques used in such instances. It incorporates informative and non-informative priors, i.e., uses information on past data and charting structures, to estimate parameters more efficiently than classical approaches. Bayesian approaches reduce the total expected cost over a finite horizon or the long-run expected average cost. This paper introduces a new Bayesian exponentially weighted moving average (EWMA) monitoring scheme for long runs based on an ARMA-GARCH model. The properties of the new monitoring scheme are investigated in terms of the run-length distribution. A case study on monitoring the USD to ZAR exchange rate is provided using the proposed Bayesian ARMA-GARCH EWMA monitoring scheme.The South African National Research Foundation (NRF), UCDP and the Research Development Programme at the University of Pretoria, Department of Research and Innovation (DRI).https://www.tandfonline.com/loi/lqen202024-07-20hj2024StatisticsSDG-08:Decent work and economic growt

Multivariate Mixed EWMA-CUSUM Control Chart for Monitoring the Process Variance-Covariance Matrix

The dispersion control charts monitor the variability of a process that may increase or decrease. An increase in dispersion parameter implies deterioration in the process for an assignable cause, while a decrease in dispersion indicates an improvement in the process. Multivariate variability control charts are used to monitor the shifts in the process variance-covariance matrix. Although multivariate EWMA and CUSUM dispersion control charts are designed to detect the small amount of change in the covariance matrix but to gain more efficiency, we have developed a Mixed Multivariate EWMA-CUSUM (MMECD) chart. The proposed MMECD chart is compared with its existing counterparts by using some important performance run length-based properties such as ARL, SDRL, EQL, SEQL, and different quantile of run length distribution. A real application related to carbon fiber tubing process is presented for practical considerations. 2013 IEEE.This work was supported by the Deanship of Scientific Research (DSR) at the King Fahd University of Petroleum and Minerals (KFUPM) under Project IN171011.Scopu

Contributions to statistical methods of process monitoring and adjustment

Ph.DDOCTOR OF PHILOSOPH