scholarly journals A Fuzzy Bivariate Poisson Control Chart

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 573 ◽  
Author(s):  
Wibawati ◽  
Muhammad Mashuri ◽  
Purhadi ◽  
Irhamah
Keyword(s):  
Parameter Estimation ◽  
Control Chart ◽  
Simulation Study ◽  
Average Run Length ◽  
Triangular Fuzzy Number ◽  
Quality Characteristic ◽  
Graphical Form ◽  
Monitoring Process ◽  
Fuzzy Parameter ◽  
Work First

In the present paper, we develop a fuzzy bivariate Poisson (FBP) control chart based on a fuzzy c chart. The FBP chart is used to monitor the sum of the nonconformities of each quality characteristic. There are two contributions of this work. First, we propose a new fuzzy parameter estimation to create a triangular fuzzy number (TFN). Second, our control chart is flexible, because we involve the α c u t to measure the level of tightness of inspection. Furthermore, the statistic of FBP is being able to visualise the monitoring process in a graphical form. In addition, the simulation study indicates that the performance of our proposed chart, based on average run length (ARL), is more sensitive than the performance of a conventional bivariate Poisson (BP) chart. Moreover, an illustration example shows that the FBP chart has relatively more sensitive performance compared to the conventional BP chart.

scholarly journals Nonparametric Double EWMA Control Chart for Process Monitoring

2016 ◽  
Vol 39 (2) ◽  
pp. 167 ◽  
Author(s):  
Muhammad Riaza ◽  
Saddam Akber Abbasib
Keyword(s):  
Standard Deviation ◽  
Control Chart ◽  
Average Run Length ◽  
Location Parameter ◽  
Quality Characteristic ◽  
Quadratic Loss ◽  
Run Length ◽  
Ewma Control Chart ◽  
Monitoring Process ◽  
Nonparametric Techniques

<p>In monitoring process parameters, we assume normality of the quality characteristic of interest, which is an ideal assumption. In many practical sit- uations, we may not know the distributional behavior of the data, and hence, the need arises use nonparametric techniques. In this study, a nonparametric double EWMA control chart, namely the NPDEWMA chart, is proposed to ensure efficient monitoring of the location parameter. The performance of the proposed chart is evaluated in terms of different run length properties, such as average, standard deviation and percentiles. The proposed scheme is compared with its recent existing counterparts, namely the nonparametric EWMA and the nonparametric CUSUM schemes. The performance mea- sures used are the average run length (ARL), standard deviation of the run length (SDRL) and extra quadratic loss (EQL). We observed that the pro- posed chart outperforms the said existing schemes to detect shifts in the process mean level. We also provide an illustrative example for practical considerations.</p>

scholarly journals Process Monitoring for Gamma Distributed Product under Neutrosophic Statistics Using Resampling Scheme

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Abdullah M. Almarashi ◽  
Muhammad Aslam
Keyword(s):  
Control Chart ◽  
Simulation Study ◽  
Average Run Length ◽  
Control Process ◽  
Practical Application ◽  
Interval Method ◽  
Length Measurements ◽  
Neutrosophic Statistics ◽  
Run Lengths ◽  
Different Levels

In this article, a repetitive sampling control chart for the gamma distribution under the indeterminate environment has been presented. The control chart coefficients, probability of in-control, probability of out-of-control, and average run lengths have been determined under the assumption of the symmetrical property of the normal distribution using the neutrosophic interval method. The performance of the designed chart has been evaluated using the average run length measurements under different process settings for an indeterminate environment. In-control and out-of-control nature of the proposed chart under different levels of shifts have been described. The comparison of the proposed chart has been made with the existing chart. A real-world example from the healthcare department has been included for the practical application of the proposed chart. It has been observed from the simulation study and real example that the proposed control chart is efficient in quick monitoring of the out-of-control process. It can be concluded that the proposed control chart can be applied effectively in uncertainty.

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 PENERAPAN METODE PENGENDALIAN KUALITAS MEWMA BERDASARKAN ARL DENGAN PENDEKATAN RANTAI MARKOV (Studi Kasus: Batik Semarang 16, Meteseh)

2021 ◽  
Vol 10 (1) ◽  
pp. 125-135
Author(s):  
Enggartya Andini ◽  
Sudarno Sudarno ◽  
Rita Rahmawati
Keyword(s):  
Markov Chain ◽  
Production Process ◽  
Control Chart ◽  
Average Run Length ◽  
Moving Average ◽  
Control Limit ◽  
Quality Characteristic ◽  
Markov Chain Approach ◽  
Mewma Control Chart ◽  
Weighted Moving Average

An industrial company requires quality control to maintain quality consistency from the production results so that it is able to compete with other companies in the world market. In the industrial sector, most processes are influenced by more than one quality characteristic. One tool that can be used to control more than one quality characteristic is the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart. The graph is used to determine whether the process has been controlled or not, if the process is not yet controlled, the next analysis that can be used is to use the Average Run Length (ARL) with the Markov Chain approach. The markov chain is the chance of today's event is only influenced by yesterday's incident, in this case the chance of the incident in question is the incident in getting a sampel of data on the production process of batik cloth to get a product that is in accordance with the company standards. ARL is the average number of sample points drawn before a point indicates an uncontrollable state. In this study, 60 sample data were used which consisted of three quality characteristics, namely the length of the cloth, the width of the cloth, and the time of the fabric for the production of written batik in Batik Semarang 16 Meteseh. Based on the results and discussion that has been done, the MEWMA controller chart uses the λ weighting which is determined using trial and error. MEWMA control chart can not be said to be stable and controlled with λ = 0.6, after calculating, the value is obtained Upper Control Limit (BKA) of 11.3864 and Lower Control Limit (BKB) of 0. It is known that from 60 data samples there is a Tj2 value that comes out from the upper control limit (BKA) where the amount of 15.70871, which indicates the production process is not controlled statistically. Improvements to the MEWMA controller chart can be done based on the ARL with the Markov Chain approach. In this final project, the ARL value used is 200, the magnitude of the process shift is 1.7 and the r value is 0.28, where the value of r is a constant obtained on the r parameter graph. The optimal MEWMA control chart based on ARL with the Markov Chain approach can be said to be stable and controlled if there is no Tj2 value that is outside the upper control limit (BKA). The results of the MEWMA control chart based on the ARL with the Markov Chain approach show that the process is not statistically capable because the MCpm value is 0.516797 and the MCpmk value is 0.437807, the value indicates a process capability index value of less than 1. Keywords: Handmade batik, Multivariate Exponentially Weighted Moving Average (MEWMA), Average Run Length (ARL), Capability process.

scholarly journals Design of a Control Chart Based on COM-Poisson Distribution for the Uncertainty Environment

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Muhammad Aslam ◽  
Ali Hussein Al-Marshadi
Keyword(s):  
Poisson Distribution ◽  
Control Chart ◽  
Simulation Study ◽  
Average Run Length ◽  
Interval Method ◽  
Interval System ◽  
Run Length ◽  
Classical Statistics ◽  
Uncertainty Environment

This paper will introduce the neutrosophic COM-Poisson (NCOM-Poisson) distribution. Then, the design of the attribute control chart using the NCOM-Poisson distribution is given. The structure of the control chart under the neutrosophic statistical interval method will be given. The algorithm to determine the average run length under neutrosophic statistical interval system will be given. The performance of the proposed control chart is compared with the chart based on classical statistics in terms of neutrosophic average run length (NARL). A simulation study and a real example are also added. From the comparison of the proposed control chart with the existing chart, it is concluded that the proposed control chart is more efficient in detecting a shift in the process. Therefore, the proposed control chart will be helpful in minimizing the defective product. In addition, the proposed control chart is more adequate and effective to apply in uncertainty environment.

scholarly journals Multiple Dependent State Repetitive Sampling-Based Control Chart for Birnbaum–Saunders Distribution

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Muhammad Aslam ◽  
Ambreen Shafqat ◽  
G. Srinivasa Rao ◽  
Jean-Claude Malela-Majika ◽  
Sandile C. Shongwe
Keyword(s):  
Control Chart ◽  
Simulation Study ◽  
Control Charts ◽  
Average Run Length ◽  
Real Life ◽  
Detection Ability ◽  
Run Length ◽  
Comprehensive Comparison ◽  
Shift Detection ◽  
Dependent State

This paper proposes a new control chart for the Birnbaum–Saunders distribution based on multiple dependent state repetitive sampling (MDSRS). The proposed control chart is a generalization of the control charts based on single sampling, repetitive sampling, and multiple dependent state sampling. Its sensitivity is evaluated in terms of the average run length (ARL) using both exact formulae and simulations. A comprehensive comparison between the Birnbaum–Saunders distribution control chart based on the MDSRS method and other existing competing methods is provided using a simulation study as well as a real-life illustration. The results reveal that the proposed chart outperforms the existing charts considered in this study by having better shift detection ability.

scholarly journals PENERAPAN DIAGRAM KENDALI MAXIMUM MULTIVARIATE CUMULATIVE SUM (MAX-MCUSUM) PADA PENGENDALIAN KUALITAS PRODUK KACANG (Studi Kasus: Produk Kacang Garing di PT XY)

2022 ◽  
Vol 10 (4) ◽  
pp. 573-582
Author(s):  
Sintia Rizki Aprilianti ◽  
Tatik Widiharih ◽  
Sudarno Sudarno
Keyword(s):  
Control Chart ◽  
Average Run Length ◽  
Quality Standards ◽  
Quality Characteristic ◽  
Statistical Quality Control ◽  
Industry Data ◽  
Machine Material ◽  
Small Shift ◽  
Pareto Chart ◽  
Main Factor

Now, Statistical quality control be a particular concern to large companies.PT XY is one of the largest nut company in Indonesia that has implemented the quality standards of a product. Max-MCUSUM control chart becomes a tool that is graphically used to monitor and evaluate whether the process is under control or nut. Based on Cheng and Thaga (2005), Max-MCUSUM control chart takes precedence over detecting small shift based on average and variability in industry data. The quality characteristic of Kacang Garing will be variables, namely broken nut skin, bean seed 1, and foam nut skin. Max-MCUSUM control chart is controlled with the control limit (h) from ARL (Average Run Length) simulation of 370 is 429,69. ARL is an average of samples that need to be decribed before it goes out of control. The research continued with multivariate capability process with MCp worth 0,905 and MCpk worth 1,355. Those value indicates that Kacang Garing has met the quality specification stipulated by PT XY. Broken nut skin becomes the most dominant cause based on pareto chart and carried out analysis by using fishbone chart so that is known the main factor causing broken nut skin are machine, material, and method. 

scholarly journals Research on Fault Diagnosis of Flexible Material R2R Manufacturing System Based on Quality Control Chart and SoV

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yaohua Deng ◽  
Na Zhou ◽  
Xiali Liu ◽  
Qiwen Lu
Keyword(s):  
Fault Diagnosis ◽  
Control Chart ◽  
Fault Location ◽  
Manufacturing System ◽  
Average Run Length ◽  
Quality Characteristic ◽  
Flexible Material ◽  
Distribution Rule ◽  
Stream Of Variation ◽  
Fault Source

The Stream of Variation (SoV) model and control chart are combined to study the fault diagnosis method of flexible materials R2R manufacturing system. Based on the analysis of the correlation between the fault source and product quality in the manufacturing process and also the statistical distribution rule of the processing quality characteristic vector Li and the fault source fi, SoV model under controlled or uncontrolled states and the mathematical model of the probability distribution of the statistic Ti,m2 of the quality characteristic variable Li are deduced. And the calculation equation of the centerline, the upper limit, and the lower limit of the control chart are deduced. The experimental results show that, under controlled or uncontrolled condition, when the program runs to 500 steps, the Average Run Length (ARL) of the performance parameters tends to be stable; and when program reaches 1000 steps, the actual ARL value is almost the same as the theoretical value. The fault diagnosis experiment shows that, under the condition when the fault source is strongly correlated or the fault source correlation coefficient is the same, using the control chart established in this paper can simply and quickly determine the fault location in the system.

scholarly journals Water Particles Monitoring in the Atacama Desert: SPC Approach Based on Proportional Data

Axioms ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 154
Author(s):  
Anderson Fonseca ◽  
Paulo Henrique Ferreira ◽  
Diego Carvalho do Nascimento ◽  
Rosemeire Fiaccone ◽  
Christopher Ulloa-Correa ◽  
...  
Keyword(s):  
Control Chart ◽  
Average Run Length ◽  
Atacama Desert ◽  
Real Data ◽  
Interval Data ◽  
Total Quality ◽  
Statistical Process ◽  
Monte Carlo Simulation Study ◽  
Run Length ◽  
Study Results

Statistical monitoring tools are well established in the literature, creating organizational cultures such as Six Sigma or Total Quality Management. Nevertheless, most of this literature is based on the normality assumption, e.g., based on the law of large numbers, and brings limitations towards truncated processes as open questions in this field. This work was motivated by the register of elements related to the water particles monitoring (relative humidity), an important source of moisture for the Copiapó watershed, and the Atacama region of Chile (the Atacama Desert), and presenting high asymmetry for rates and proportions data. This paper proposes a new control chart for interval data about rates and proportions (symbolic interval data) when they are not results of a Bernoulli process. The unit-Lindley distribution has many interesting properties, such as having only one parameter, from which we develop the unit-Lindley chart for both classical and symbolic data. The performance of the proposed control chart is analyzed using the average run length (ARL), median run length (MRL), and standard deviation of the run length (SDRL) metrics calculated through an extensive Monte Carlo simulation study. Results from the real data applications reveal the tool’s potential to be adopted to estimate the control limits in a Statistical Process Control (SPC) framework.

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