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.

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.

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 The Use of Statistical Process Control Tools for Analysing Financial Statements

2017 ◽  
Vol 17 (1) ◽  
pp. 129-137
Author(s):  
Janusz Niezgoda
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Financial Statements ◽  
Arithmetic Mean ◽  
Statistical Process ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Financial Analyses

Abstract This article presents the proposed application of one type of the modified Shewhart control charts in the monitoring of changes in the aggregated level of financial ratios. The control chart x̅ has been used as a basis of analysis. The examined variable from the sample in the mentioned chart is the arithmetic mean. The author proposes to substitute it with a synthetic measure that is determined and based on the selected ratios. As the ratios mentioned above, are expressed in different units and characters, the author applies standardisation. The results of selected comparative analyses have been presented for both bankrupts and non-bankrupts. They indicate the possibility of using control charts as an auxiliary tool in financial analyses.

scholarly journals COMPARAÇÃO ENTRE GRÁFICOS DE CONTROLE DE SHEWHART, CUSUM E MMEP NO PROCESSO DE IRRIGAÇÃO POR ASPERSÃO CONVENCIONAL

Irriga ◽  
2018 ◽  
Vol 1 (01) ◽  
pp. 56 ◽  
Author(s):  
JIAM PIRES FRIGO ◽  
MARCIO ANTONIO VILAS BOAS ◽  
JIANICE PIRES FRIGO ◽  
ELISANDRO PIRES FRIGO
Keyword(s):  
Quality Control ◽  
Wind Speed ◽  
Control Charts ◽  
Irrigation System ◽  
Arima Model ◽  
Sprinkler Irrigation ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Quality Control Process ◽  
Shewhart Charts

COMPARAÇÃO ENTRE GRÁFICOS DE CONTROLE DE SHEWHART, CUSUM E MMEP NO PROCESSO DE IRRIGAÇÃO POR ASPERSÃO CONVENCIONAL  JIAM PIRES FRIGO1; MARCIO ANTONIO VILAS BOAS2; JIANICE PIRES FRIGO2 E ELISANDRO PIRES FRIGO3 1 Instituto Latino-Americano de Tecnologia, Infraestrutura e Território - ILATIT (UNILA), Av. Tancredo Neves, 3838 - Porto Belo, CEP 85867-970, Foz do Iguaçu, PR, Fone (45)99993 4783, e‑mail:[email protected] Centro de Ciências Exatas e Tecnológicas, Programa de Pós Graduação em Engenharia Agrícola (Unioeste), R. Universitária, 2069 - Jardim Universitário CEP 85819-110, Cascavel-PR3 Universidade Federal do Paraná, Campus Palotina  R. Pioneiro, 2153 - Dallas, CEP 85950-000, Palotina-PR  1 RESUMO O objetivo deste estudo foi comparar os resultados da utilização dos gráficos de controle de Shewhart para medidas individuais, com os gráficos de controle média móvel exponencialmente ponderada (MMEP) e soma cumulativa (CUSUM), aplicados no controle de qualidade da irrigação. Foram realizados 60 ensaios de irrigação em um sistema por aspersão convencional. As análises do processo de controle de qualidade do sistema de irrigação foram realizadas por meio dos gráficos de Shewhart (Xbarra), gráficos MMEP e CUSUM. Todos os procedimentos para os ensaios de uniformidade da irrigação foram realizados conforme recomendação NBR ISO 7749-2 (ABNT, 2000). Para a avaliação do sistema foi utilizado o Coeficiente de Uniformidade de Christiansen (CUC). O gráfico de controle MMEP apresentou-se bastante suscetível quando utilizado em dados auto correlacionados, com ocorrências de alarmes falsos. Para dados independentes (pelo modelo ARIMA), o gráfico CUSUM foi mais sensível ao detectar as variações ocorridas na irrigação devido à velocidade do vento, quando comparado aos gráficos MMEP e Shewhart para os mesmos dados. Na irrigação por aspersão, relacionando CUC com velocidade do vento, o gráfico de Shewhart foi mais indicado pela simplicidade, robustez e facilidade de interpretação, mesmo na presença de dados que violam a suposição de independência. Os gráficos de controle de Shewhart, MMEP e CUSUM provaram serem ótimas ferramentas estatísticas no estudo da irrigação por aspersão convencional, demonstrando muito bem a variabilidade no processo. Palavras-Chave: água, vento, Coeficiente de Christiansen, Controle de qualidade.  FRIGO, J.P.; VILAS BOAS, M.A.; FRIGO, J.P.; FRIGO, E.P.COMPARISON BETWEEN SHEWHART CONTROL CHARTS, CUSUM AND MMEP IN PROCESS OF CONVENTIONAL IRRIGATION SPRINKLER  2 ABSTRACT This study aimed to compare the results of Shewhart control charts use for individual measures with exponentially weighted moving average (MMEP) and cumulative sum (CUSUM) control charts applied in quality control of conventional sprinkler irrigation. Sixty irrigation trials were set up in a conventional sprinkler system. The analyses of the quality control process of the irrigation system were performed by means of Shewhart charts (Xbarra) charts, MMEP and CUSUM. All procedures for testing uniformity of irrigation were performed as recommended by ISO 7749-2 (ABNT, 2000). For the evaluation of the system, it was used Christiansen Uniformity Coefficient (CUC). The control chart MMEP showed to be quite susceptible when used in auto correlated data with instances of false alarms. For independent data (the ARIMA model), the CUSUM tabular chart was more sensitive to detect variations in irrigation due to wind speed, when compared to MMEP and Shewhart charts for the same data. In sprinkler irrigation, relating CUC with wind speed, the Shewhart chart was better due to such features as simplicity, robustness and easiness of interpretation, even in the presence of data that violate the assumption of independence. The Shewhart control charts, CUSUM and MMEP statistics proved to be great tools in the study of irrigation sprinkler, demonstrating very well the variability in the process. Keywords: water, wind, Christiansen coefficient, control charts, Quality control.

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.

Research in Fuzzy Quality Control Chart Base on Similarity Index

2012 ◽  
Vol 197 ◽  
pp. 50-54
Author(s):  
Jing Wen An ◽  
Ying Bai
Keyword(s):  
Quality Control ◽  
Fuzzy Control ◽  
Control Chart ◽  
Similarity Index ◽  
Fuzzy Similarity ◽  
Quality Control Chart ◽  
And Control ◽  
Real Product

This paper is focusing on the application of control chart based on fuzzy similarity in the monitor of products quality. Methodologically, this paper verified a way that transferring the form of real product dates to that can be used in fuzzy control chart, and, moreover, successfully calculated and traced and control chart.

scholarly journals A Nonparametric Shewhart-Type Quality Control Chart for Monitoring Broad Changes in a Process Distribution

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Saad T. Bakir
Keyword(s):  
Quality Control ◽  
Control Chart ◽  
Control Charts ◽  
Probability Distributions ◽  
Average Run Length ◽  
Training Sample ◽  
Statistical Quality Control ◽  
Test Statistic ◽  
Run Length ◽  
Quality Control Chart

This paper develops a distribution-free (or nonparametric) Shewhart-type statistical quality control chart for detecting a broad change in the probability distribution of a process. The proposed chart is designed for grouped observations, and it requires the availability of a reference (or training) sample of observations taken when the process was operating in-control. The charting statistic is a modified version of the two-sample Kolmogorov-Smirnov test statistic that allows the exact calculation of the conditional average run length using the binomial distribution. Unlike the traditional distribution-based control charts (such as the Shewhart X-Bar), the proposed chart maintains the same control limits and the in-control average run length over the class of all (symmetric or asymmetric) continuous probability distributions. The proposed chart aims at monitoring a broad, rather than a one-parameter, change in a process distribution. Simulation studies show that the chart is more robust against increased skewness and/or outliers in the process output. Further, the proposed chart is shown to be more efficient than the Shewhart X-Bar chart when the underlying process distribution has tails heavier than those of the normal distribution.

The Application of Quality Control Chart Based on Minitab in the Processing of Camshafts

2014 ◽  
Vol 678 ◽  
pp. 712-715
Author(s):  
Si Wei Liu ◽  
Yi Kai Wang ◽  
Heng Bao Xin
Keyword(s):  
Quality Control ◽  
Confidence Interval ◽  
Mathematical Models ◽  
Control Chart ◽  
Control Charts ◽  
The Mean ◽  
Quality Control Chart

For camshafts’ processing problems, the samples extracted from processes for its production were analyzed, and then Minitab software was used to draw the mean-range control charts, and to evaluate its processing state. In this paper, the key process was analyzed, and its overall sample confidence interval was identified. Finally, the eligibility of this sample batch was judged based on the mathematical models established as follows.

Sign in / Sign up

Export Citation Format

Share Document