Monitoring process control chart with finite mixture probability distribution

2018 ◽  
Vol 35 (2) ◽  
pp. 335-353 ◽  
Author(s):  
Damaris Serigatto Vicentin ◽  
Brena Bezerra Silva ◽  
Isabela Piccirillo ◽  
Fernanda Campos Bueno ◽  
Pedro Carlos Oprime
Keyword(s):  
Probability Distribution ◽  
Control Chart ◽  
Control Charts ◽  
Probability Distributions ◽  
Mixture Distribution ◽  
Quality Parameters ◽  
Finite Mixture ◽  
Statistical Parameters ◽  
Content Type ◽  
Monitoring Process

Purpose The purpose of this paper is to develop a monitoring multiple-stream processes control chart with a finite mixture of probability distributions in the manufacture industry. Design/methodology/approach Data were collected during production of a wheat-based dough in a food industry and the control charts were developed with these steps: to collect the master sample from different production batches; to verify, by graphical methods, the quantity and the characterization of the number of mixing probability distributions in the production batch; to adjust the theoretical model of probability distribution of each subpopulation in the production batch; to make a statistical model considering the mixture distribution of probability and assuming that the statistical parameters are unknown; to determine control limits; and to compare the mixture chart with traditional control chart. Findings A graph was developed for monitoring a multi-stream process composed by some parameters considered in its calculation with similar efficiency to the traditional control chart. Originality/value The control chart can be an efficient tool for customers that receive product batches continuously from a supplier and need to monitor statistically the critical quality parameters.

Comparing Robustness of EWMA Dispersion Control Chart for Non-Normal Process

2014 ◽  
Vol 912-914 ◽  
pp. 1189-1192
Author(s):  
Hai Yu Wang
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Normal Process ◽  
Comparison Analysis ◽  
Run Length ◽  
Monitoring Process ◽  
Process Dispersion

This article discusses robustness to non-normality of EWMA charts for dispersion. Comparison analysis of run length of four kinds of EWMA charts to monitoring process dispersion is provided to evaluate control charts performance and robustness. At last robust EWMA dispersion charts for non-normal processes are proposed by this way.

Fuzzy U control chart based on fuzzy rules and evaluating its performance using fuzzy OC curve

2018 ◽  
Vol 30 (3) ◽  
pp. 232-247 ◽  
Author(s):  
Somayeh Fadaei ◽  
Alireza Pooya
Keyword(s):  
Fuzzy Control ◽  
Sample Size ◽  
Control Chart ◽  
Control Charts ◽  
Operating Characteristic ◽  
Fuzzy Rules ◽  
Type Ii ◽  
Type Ii Error ◽  
Content Type ◽  
Variable Sample Size

Purpose The purpose of this paper is to apply fuzzy spectrum in order to collect the vague and imprecise data and to employ the fuzzy U control chart in variable sample size using fuzzy rules. This approach is improved and developed by providing some new rules. Design/methodology/approach The fuzzy operating characteristic (FOC) curve is applied to investigate the performance of the fuzzy U control chart. The application of FOC presents fuzzy bounds of operating characteristic (OC) curve whose width depends on the ambiguity parameter in control charts. Findings To illustrate the efficiency of the proposed approach, a practical example is provided. Comparing performances of control charts indicates that OC curve of the crisp chart has been located between the FOC bounds, near the upper bound; as a result, for the crisp control chart, the probability of the type II error is of significant level. Also, a comparison of the crisp OC curve with OCavg curve and FOCα curve approved that the probability of the type II error for the crisp chart is more than the same amount for the fuzzy chart. Finally, the efficiency of the fuzzy chart is more than the crisp chart, and also it timely gives essential alerts by means of linguistic terms. Consequently, it is more capable of detecting process shifts. Originality/value This research develops the fuzzy U control chart with variable sample size whose output is fuzzy. After creating control charts, performance evaluation in the industry is important. The main contribution of this paper is to employs the FOC curve for evaluating the performance of the fuzzy control chart, while in prior studies in this area, the performance of fuzzy control chart has not been evaluated.

Robust approaches for monitoring logistic regression profiles under outliers

2017 ◽  
Vol 34 (4) ◽  
pp. 494-507 ◽  
Author(s):  
Ahmad Hakimi ◽  
Amirhossein Amiri ◽  
Reza Kamranrad
Keyword(s):  
Logistic Regression ◽  
Robust Control ◽  
Phase I ◽  
Control Chart ◽  
Phase Ii ◽  
Control Charts ◽  
Likelihood Estimation ◽  
Estimation Of Parameters ◽  
Content Type ◽  
Practical Applications

Purpose The purpose of this paper is to develop some robust approaches to estimate the logistic regression profile parameters in order to decrease the effects of outliers on the performance of T2 control chart. In addition, the performance of the non-robust and the proposed robust control charts is evaluated in Phase II. Design/methodology/approach In this paper some, robust approaches including weighted maximum likelihood estimation, redescending M-estimator and a combination of these two approaches (WRM) are used to decrease the effects of outliers on estimating the logistic regression parameters as well as the performance of the T2 control chart. Findings The results of the simulation studies in both Phases I and II show the better performance of the proposed robust control charts rather than the non-robust control chart for estimating the logistic regression profile parameters and monitoring the logistic regression profiles. Practical implications In many practical applications, there are outliers in processes which may affect the estimation of parameters in Phase I and as a result of deteriorate the statistical performance of control charts in Phase II. The methods developed in this paper are effective for decreasing the effect of outliers in both Phases I and II. Originality/value This paper considers monitoring the logistic regression profile in Phase I under the presence of outliers. Also, three robust approaches are developed to decrease the effects of outliers on the parameter estimation and monitoring the logistic regression profiles in both Phases I and II.

scholarly journals A Robust Control Chart for Monitoring Dispersion

2013 ◽  
Vol 2013 ◽  
pp. 1-5
Author(s):  
Maoyuan Zhou ◽  
Wei Geng
Keyword(s):  
Robust Control ◽  
Control Chart ◽  
Control Charts ◽  
Nonparametric Test ◽  
Dispersion Parameters ◽  
Monitoring Process ◽  
Location Parameters ◽  
Process Dispersion ◽  
Change Point Model ◽  
Process Location

Most robust control charts in the literature are for monitoring process location parameters, such as mean or median, rather than process dispersion parameters. This paper develops a new robust control chart by integrating a two-sample nonparametric test into the effective change-point model. Our proposed chart is easy in computation, convenient to use, and very powerful in detecting process dispersion shifts.

Research on the quality stability evaluation and monitoring based on the pre-control chart

2014 ◽  
Vol 31 (9) ◽  
pp. 966-982 ◽  
Author(s):  
Shu Qing Liu ◽  
Qin Su ◽  
Ping Li
Keyword(s):  
Sample Size ◽  
Production Process ◽  
Control Chart ◽  
Control Charts ◽  
Stability Evaluation ◽  
Monitoring And Control ◽  
Content Type ◽  
Quality Stability ◽  
Evaluation And Monitoring ◽  
And Control

Purpose – In order to meet the requirements of 6σ management and to overcome the deficiencies of the theory for using the pre-control chart to evaluate and monitor quality stability, the purpose of this paper is to probe into the quality stability evaluation and monitoring guidelines of small batch production process based on the pre-control chart under the conditions of the distribution center and specifications center non-coincidence (0<ɛ≤1.5σ), the process capability index C p ≥2 and the virtual alarm probability α=0.27 percent. Design/methodology/approach – First, the range of the quality stability evaluation sampling number in initial production process is determined by using probability and statistics methods, the sample size for the quality stability evaluation is adjusted and determined in initial production process according to the error judgment probability theory, and the guideline for quality stability evaluation has been proposed in initial production process based on the theory of small probability events. Second, the alternative guidelines for quality stability monitoring and control in formal production process are proposed by using combination theory, the alternative guidelines are initially selected based on the theory of small probability events, a comparative analysis of the guidelines is made according to the average run lengths values, and the monitoring and control guidelines for quality stability are determined in formal production process. Findings – The results obtained from research indicate that when the virtual alarm probability α=0.27 percent, the shifts ɛ in the range 0<ɛ≤1.5σ and the process capability index C p ≥2, the quality stability evaluation sample size of the initial production process is 11, whose scondition is that the number of the samples falling into the yellow zone is 1 at maximum. The quality stability evaluation sample size of the formal production process is 5, and when the number of the samples falling into the yellow zone is ≤1, the process is stable, while when two of the five samples falling into the yellow, then one more sample needs to be added, and only if this sample falls into the green zone, the process is stable. Originality/value – Research results can overcome the unsatisfactory 6σ management assumptions and requirements and the oversize virtual alarm probability α of the past pre-control charts, as well as the shortage only adaptable to the pre-control chart when the shifts ɛ=0. And at the same time, the difficult problem hard to adopt the conventional control charts to carry out process control because of a fewer sample sizes is solved.

Performance of adaptive np-chart with estimated parameter

2016 ◽  
Vol 33 (6) ◽  
pp. 769-791 ◽  
Author(s):  
S. Mohammad Hashemian ◽  
Rassoul Noorossana ◽  
Ali Keyvandarian ◽  
Maryam Shekary A.
Keyword(s):  
Phase I ◽  
Control Chart ◽  
Performance Measures ◽  
Control Charts ◽  
Process Parameter ◽  
The Body ◽  
Content Type ◽  
Average Time To Signal ◽  
Body Of Knowledge ◽  
First Time

Purpose – The purpose of this paper is to compare the performances of np-VP control chart with estimated parameter to the np-VP control chart with known parameter using average time-to-signal (ATS), standard deviation of the time-to-signal (SDTS), and average number of observations to signal (ANOS) as performance measures. Design/methodology/approach – The approach used in this study is probabilistic in which the expected values of performance measures are calculated using probabilities of different estimators used to estimate process parameter. Findings – Numerical results indicate different performances for the np-VP control chart in known and estimated parameter cases. It is obvious that when process parameter is not known and is estimated using Phase I data, the chart does not perform as user expects. To tackle this issue, optimal Phase I estimation scenarios are recommended to obtain the best performance from the chart in the parameter estimation case in terms of performance measures. Practical implications – This research adds to the body of knowledge in quality control of process monitoring systems. This paper may be of particular interest to practitioners of quality systems in factories where products are monitored to reduce the number of defectives and np chart parameter needs to be estimated. Originality/value – The originality of this paper lies within the context in which an adaptive np control chart is studied and the process parameter unlike previous studies is assumed unknown. Although other types of control charts have been studied when process parameter is unknown but this is the first time that adaptive np chart performance with estimated process parameter is studied.

Statistical Process Control on Time Delay Feedback Adjustment Process

2011 ◽  
Vol 211-212 ◽  
pp. 305-309
Author(s):  
Hai Yu Wang
Keyword(s):  
Time Delay ◽  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Adjustment Process ◽  
Statistical Process ◽  
Controlled Processes ◽  
Monitoring Process ◽  
Set Up ◽  
And Control

Control chart can be designed to quickly detect small shifts in the mean of a sequence of independent normal observations. But this chart cannot perform well for autocorrelated process. The main goal of this article is to suggest a control chart method using to monitoring process with different time delay feedback controlled processes. A quality control model based on delay feedback controlled processes is set up. And the calculating method of average run length of control charts based on process output and control action of multiple steps delay MMSE feedback controlled processes is provided to evaluate control charts performance. A simple example is used to illustrate the procedure of this approach.

scholarly journals A Study of Cumulative Quantity Control Chart for a Mixture of Rayleigh Model under a Bayesian Framework

2016 ◽  
Vol 39 (2) ◽  
pp. 185 ◽  
Author(s):  
Muhammad Aslam ◽  
Muhammad Riaz ◽  
Tabassum Naz Sindhu ◽  
Zaheer Ahmed
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Geometric Model ◽  
Mixture Distribution ◽  
Cumulative Distribution ◽  
Statistical Control ◽  
Design Structure ◽  
Weibull Models ◽  
Cumulative Count ◽  
Quantity Control

<p>This study deals with the cumulative charting technique based on a simple and a mixture of Rayleigh models. The respective charting schemes are referred as the SRCQC-chart and the MRCQC-chart. These are stimulated from existing statistical control charts in this direction i.e. the cumulative quantity control (CQC) chart, based on exponential and Weibull models, and the cumulative count control (CCC) chart, based on the simple geometricmodel. Another motivation for this study is the mixture cumulative count control (MCCC) chart based on the two component geometric model. The use of mixture cumulative quantity is an attractive approach for process monitoring. The design structure of the proposed control chart is derived by using the cumulative distribution function of simple, and two components of mixture distribution(s). We observed that the proposed charting structure is efficient in detecting the changes in process parameters. The application of the proposed scheme is illustrated using a real dataset.</p>

Social choice using moral metrics

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kenneth Halpern
Keyword(s):  
Probability Distribution ◽  
Social Choice ◽  
Design Methodology ◽  
Probability Distributions ◽  
New Paradigm ◽  
Content Type ◽  
Practical Applications ◽  
Moral Systems ◽  
Black Boxes ◽  
Multidimensional Geometry

PurposeThis paper aims to develop a geometry of moral systems. Existing social choice mechanisms predominantly employ simple structures, such as rankings. A mathematical metric among moral systems allows us to represent complex sets of views in a multidimensional geometry. Such a metric can serve to diagnose structural issues, test existing mechanisms of social choice or engender new mechanisms. It also may be used to replace active social choice mechanisms with information-based passive ones, shifting the operational burden.Design/methodology/approachUnder reasonable assumptions, moral systems correspond to computational black boxes, which can be represented by conditional probability distributions of responses to situations. In the presence of a probability distribution over situations and a metric among responses, codifying our intuition, we can derive a sensible metric among moral systems.FindingsWithin the developed framework, the author offers a set of well-behaved candidate metrics that may be employed in real applications. The author also proposes a variety of practical applications to social choice, both diagnostic and generative.Originality/valueThe proffered framework, derived metrics and proposed applications to social choice represent a new paradigm and offer potential improvements and alternatives to existing social choice mechanisms. They also can serve as the staging point for research in a number of directions.

A regression spline control chart for monitoring characteristics exhibiting nonlinear profile over time

2019 ◽  
Vol 31 (3) ◽  
pp. 507-522 ◽  
Author(s):  
Boby John ◽  
Vaibhav Agarwal
Keyword(s):  
Control Chart ◽  
Power Distribution ◽  
Control Charts ◽  
Time Profile ◽  
Center Line ◽  
Response Variable ◽  
Content Type ◽  
Line Segments ◽  
Regression Spline ◽  
Over Time

Purpose The purpose of this paper is to demonstrate the application of the control chart procedure to monitor the characteristics whose profile over time resembles a set of connected line segments. Design/methodology/approach Fit a regression spline model by taking the subgroup average of the characteristic as response variable and time as the explanatory variable. Then monitor the response variable using the regression spline control chart with the fitted model as center line and upper and lower control limits at three standard deviation units of the response variable above and below the center line. Findings The proposed chart is successfully deployed to monitor the daily response time profile of a client server of an application support process. The chart ensured the stability of the process as well as detected the assignable cause leading to the slowing down of the server performance. Practical implications The methodology can be used to monitor any characteristics whose performance profile over time resembles a set of connected line segments. Some of the examples are the consumption profile of utility providers like power distribution companies, usage profiles of telecom networks, loading profile of airline check-in process, e-commerce websites, etc. Originality/value To the best of the author’s knowledge, construction of control charts using regression spline is new. The usage of the control chart to monitor the performance characteristics which exhibits a nonlinear profile over time is also rare.

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