scholarly journals Design of a Control Chart Using Extended EWMA Statistic

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 108 ◽  
Author(s):  
Muhammad Naveed ◽  
Muhamma Azam ◽  
Nasrullah Khan ◽  
Muhammad Aslam
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Moving Average ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Application Data ◽  
Mean And Variance ◽  
The Mean ◽  
Working Procedure

In the present paper, we propose a control chart based on extended exponentially weighted moving average (EEWMA) statistic to detect a quick shift in the mean. The mean and variance expression of the proposed EEWMA statistic are derived. The proposed EEWMA statistic is unbiased and simulation results show a smaller variance as compared to the traditional EWMA. The performance of the proposed control chart with the existing chart based on the EWMA statistic is evaluated in terms of average run length (ARL). Various tables were constructed for different values of parameters. The comparison of the EEWMA control chart with the traditional EWMA and Shewhart control charts illustrates that the proposed control chart performs better in terms of quick detection of the shift. The working procedure of the proposed control chart was also illustrated by simulated and application data.

scholarly journals A Nonparametric HEWMA-p Control Chart for Variance in Monitoring Processes

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 356 ◽  
Author(s):  
Muhammad Aslam ◽  
G. Rao ◽  
Ali AL-Marshadi ◽  
Chi-Hyuck Jun
Keyword(s):  
Normal Distribution ◽  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Moving Average ◽  
Asymmetric Distribution ◽  
Unknown Distribution ◽  
Shewhart Control ◽  
Average Proportion ◽  
Significant Measure

Control charts are considered as powerful tools in detecting any shift in a process. Usually, the Shewhart control chart is used when data follows the symmetrical property of a normal distribution. In practice, the data from the industry may follow a non-symmetrical distribution or an unknown distribution. The average run length (ARL) is a significant measure to assess the performance of the control chart. The ARL may mislead when the statistic is computed from an asymmetric distribution. To handle this issue, in this paper, an ARL-unbiased hybrid exponentially weighted moving average proportion (HEWMA-p) chart is proposed for monitoring the process variance for a non-normal distribution or an unknown distribution. The efficiency of the proposed chart is compared with the existing chart in terms of ARLs. The proposed chart is more efficient than the existing chart in terms of ARLs. A real example is given for the illustration of the proposed chart in the industry.

SHEWHART CHARTS FOR THE DETECTION OF LINEAR TRENDS AND OF SHIFTS IN AN AUTOREGRESSION MODEL

2000 ◽  
Vol 07 (04) ◽  
pp. 309-330 ◽  
Author(s):  
RAINER GÖB
Keyword(s):  
Control Charts ◽  
Linear Trend ◽  
Average Run Length ◽  
Model Parameters ◽  
Adjustment Policy ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Autoregression Model ◽  
The Mean ◽  
Biased Estimates

The paper considers a univariate characteristic of a manufacturing process which is measured at discrete time points. The characteristic exhibits a linear trend under an AR(1) disturbance. If the slope of the linear trend and the autoregression coefficient are known, the process characteristic can be adjusted to vary as white noise around its target. However, the adjustment policy is very sensitive to departures from model assumptions and fails to achieve its objective in case of shifted model parameters, e.g., in case of biased estimates or external assignable causes which change the parameters. A discussion of the behaviour of the adjusted process shows that parameter shifts can have harmful consequences. As a protection against parameter shifts, additional statistical monitoring of the process is indispensable. The paper introduces various Shewhart control charts for the detection of shifts in the mean, the trend parameter, or the autoregression parameter. The performance of the charts is analyzed by the average run length criterion.

scholarly journals Control charts for monitoring drip irrigation with different hydraulic heads

2020 ◽  
Vol 15 (4) ◽  
pp. 1
Author(s):  
Allan Remor Lopes ◽  
Marcio Antonio Vilas Boas ◽  
Felix Augusto Pazuch ◽  
Diane Aparecida Ostroski ◽  
Marta Juliana Schmatz
Keyword(s):  
Control Chart ◽  
Drip Irrigation ◽  
Control Charts ◽  
Irrigation System ◽  
Moving Average ◽  
Drip Irrigation System ◽  
State University ◽  
Shewhart Control Charts ◽  
Agricultural Engineering ◽  
Shewhart Control

This study monitored a drip irrigation system with different hydraulic heads, using control charts. The study included 25 tests, and was conducted at the Experimental Nucleus of Agricultural Engineering of the State University of Western Paraná, located in the municipality of Cascavel, Paraná. The drip irrigation system was operated by gravity, and had four hydraulic heads (10, 11, 12 and 15 kPa). The uniformity of the system was determined based on uniformity distribution. Uniformity monitoring was performed using Shewhart and exponentially weighted moving-average (EWMA) control charts. An increase in the hydraulic head increased uniformity. The use of 12 and 15 kPa hydraulic heads yielded good performance, whereas 10 and 11 kPa yielded regular performance. The use of control charts proved to be efficient; the Shewhart control chart was more robust, whereas the EWMA control chart, which indicated trends and deviations not shown by Shewhart control charts, was more sensitive.

The Enhanced EWMA Control Chart with Autocorrelation

2014 ◽  
Vol 71 (5) ◽  
Author(s):  
Abbas Umar Farouk ◽  
Ismail Mohamad
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Independence Assumption ◽  
Shewhart Control Charts ◽  
Ewma Chart ◽  
Ewma Control Chart ◽  
Shewhart Control ◽  
Autocorrelated Data ◽  
The Given

Control charts are effective tool with regard to improving process quality and productivity, Shewhart control charts are efficiently good at detecting large shifts in a given process but very slow in detecting small and moderate shifts, such problem could be tackled through design of sensitizing rules. It has been observed that autocorrelation has an advert effect on the control charts developed under the independence assumption [1]. In this article a new EWMA control chart has been introduced with autocorrelation and some run rule schemes were introduced to enhanced the performance of the EWMA control chart when autocorrelated. The three-out-of three EWMA scheme and three-out-of- four EWMA schemes were introduced and the generated data with induced autocorrelation were used to construct the EWMA chart to sensitize the shifts presence.  Simulation of autocorrelated data were carried out for the proposed schemes which detects the shifts as soon as it occurs in the given process, the performance were evaluated using the ARL (average run length) and the results were compared with the published results of Steiner (1991) and the Saccucci (1990) which were designed for large, small and moderate shift. The results indicates that the proposed schemes are more sensitive to the shifts at ARL0=500, 300 and 200 with autocorrelation of 0.2, 0.5 and 0.9 considered in the study.

PERFORMANCE OF SENSITIZING RULES ON SHEWHART CONTROL CHARTS WITH AUTOCORRELATED DATA

2001 ◽  
Vol 08 (02) ◽  
pp. 159-171 ◽  
Author(s):  
SANDY D. BALKIN ◽  
DENNIS K. J. LIN
Keyword(s):  
Time Series ◽  
Control Charts ◽  
Moving Average ◽  
Correlated Data ◽  
Autoregressive Moving Average ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
The Mean ◽  
Autocorrelated Data ◽  
Using Data

Sensitizing Rules are commonly applied to Shewhart Charts to increase their effectiveness in detecting shifts in the mean that may otherwise go unnoticed by the usual "out-of-control" signals. The purpose of this paper is to demonstrate how well these rules actually perform when the data exhibit autocorrelation compared to non-correlated data. Since most control chart data are collected as time series, it is of interest to examine the performance of Shewhart's [Formula: see text] Chart using data generated from typical time series models. In this paper, measurements arising from autoregressive (AR), moving average (MA) and autoregressive moving average (ARMA) processes are examined using Shewhart Control Charts in conjunction with several sensitizing rules. The results indicate that the rules work well when there are strong autocorrelative relationships, but are not as effective in recognizing small to moderate levels of correlation. We conclude with the recommendation to practitioners that they use a more definitive measure of autocorrelation such as the Sample Autocorrelation Function correlogram to detect dependency.

scholarly journals A Fuzzy Control Chart Approach for Attributes and Variables

2018 ◽  
Vol 8 (5) ◽  
pp. 3360-3365 ◽  
Author(s):  
N. Pekin Alakoc ◽  
A. Apaydin
Keyword(s):  
Quality Control ◽  
Fuzzy Control ◽  
Control Chart ◽  
Control Charts ◽  
Fuzzy Numbers ◽  
Fuzzy Theory ◽  
New Approach ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Quality Control Chart

The purpose of this study is to present a new approach for fuzzy control charts. The procedure is based on the fundamentals of Shewhart control charts and the fuzzy theory. The proposed approach is developed in such a way that the approach can be applied in a wide variety of processes. The main characteristics of the proposed approach are: The type of the fuzzy control charts are not restricted for variables or attributes, and the approach can be easily modified for different processes and types of fuzzy numbers with the evaluation or judgment of decision maker(s). With the aim of presenting the approach procedure in details, the approach is designed for fuzzy c quality control chart and an example of the chart is explained. Moreover, the performance of the fuzzy c chart is investigated and compared with the Shewhart c chart. The results of simulations show that the proposed approach has better performance and can detect the process shifts efficiently.

A control chart for severity index to detect drought compares favourably to logistic regression

2011 ◽  
Vol 42 (1) ◽  
pp. 1-9
Author(s):  
Jared Townsley ◽  
Justin R Chimka
Keyword(s):  
Logistic Regression ◽  
Control Chart ◽  
Control Charts ◽  
Severity Index ◽  
Drought Severity ◽  
Prediction Errors ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Accessible Method ◽  
Made In

We describe the discovery of how a traditional control chart for the Palmer Drought Severity Index (PDSI) to detect drought compares favourably to a theoretically appropriate statistical (logistic regression) model of drought as a function of PDSI. Our empirical results are based on monthly observations of PDSI, precipitation and temperature made in Kansas since 1895. Results from the study suggest that a relatively simple statistical approach based on Shewhart control charts may provide a more accessible method for relevant government agencies to predict droughts, improving resource management and preparation. Moreover, utilizing such an approach over more sophisticated methods may come at little expense regarding prediction errors.

Single-Subgroup Performance Measures and Diagnostic Procedures for X-Bar Control Charts

1994 ◽  
Vol 116 (2) ◽  
pp. 216-224
Author(s):  
G. E. Rahn ◽  
S. G. Kapoor ◽  
R. E. DeVor
Keyword(s):  
Performance Measures ◽  
Control Charts ◽  
Simulated Data ◽  
Diagnostic Procedures ◽  
Shewhart Control Charts ◽  
Operational Characteristics ◽  
Shewhart Control ◽  
The Mean ◽  
Measures Of Performance ◽  
Tremendous Impact

Although Shewhart control charts have had a tremendous impact on quality improvement, the inability to precisely measure chart performance has limited their role, and subsequently overall effectiveness in the control of manufacturing processes. Measures of performance in terms of operational characteristics (OC) are defined on two distinct levels: (a) single-subgroup level, which examines the probability of a rule violation at any given subgroup (b) multiple-subgroup level, which considers the probability of one or more rule violations throughout process monitoring. Single-subgroup performance measures for X-bar charts that employ four rules are formulated. These measures are exact expressions of operational characteristics, except for the numerical approximation to the integral of the normal distribution. Applications of these models to simulated data demonstrate their accuracy in predicting chart performance. In addition, a diagnostic methodology is described which utilizes the derived performance measures to predict the mean of a shifted distribution. The proposed diagnostic procedure is illustrated in validation and application examples.

Improving Benchmarking Student Learning Outcomes Using Repetitive Sampling Control Chart

2016 ◽  
Vol 13 (10) ◽  
pp. 7036-7039
Author(s):  
Nawal G Alghamdi ◽  
Muhammad Aslam
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Assessment Tool ◽  
Educational Programs ◽  
Management Program ◽  
Pass Rate ◽  
Shewhart Control Charts ◽  
Process Understanding ◽  
Shewhart Control

In recent years research on the application of Shewhart control charts in evaluating the performance of educational programs have gain sufficient grounds. These control charts aid in process understanding and identify changes that indicate either improvement or deterioration in quality of the program. Current research proposes control charts using repetitive sampling on the data taken from Weber State University’s construction management program, which uses the Associate Constructor Level 1 exam as an assessment tool. A code was developed to run the proposed control charts. Both the traditional and proposed charts were plotted using R software. The results indicate that the proposed control charts are comparatively more efficient than the traditional control charts in assessment of educational programs and minimizing false positives. At the end comparison of the benchmark—pass rate and traditional control chart with the proposed control chart has also been elucidated so that the proposed control charts may be readily employed in evaluating any educational program by academic counsellors.

Design of a Variable Sampling Interval EWMA Median Control Chart

2019 ◽  
Vol 26 (05) ◽  
pp. 1950021 ◽  
Author(s):  
Kim Phuc Tran ◽  
Philippe Castagliola ◽  
Thi Hien Nguyen ◽  
Anne Cuzol
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Food Production ◽  
Average Run Length ◽  
Moving Average ◽  
Sampling Interval ◽  
Performance Comparison ◽  
Variable Sampling Interval ◽  
Variable Sampling ◽  
Weighted Moving Average

In the literature, median type control charts have been widely investigated as easy and efficient means to monitor the process mean when observations are from a normal distribution. In this work, a Variable Sampling Interval (VSI) Exponentially Weighted Moving Average (EWMA) median control chart is proposed and studied. The Markov chains are used to calculate the average run length to signal (ARL). A performance comparison with the original EWMA median control chart is made. The numerical results show that the proposed chart is considerably more effective as it is faster in detecting process shifts. Finally, the implementation of the proposed chart is illustrated with an example in food production process.

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