scholarly journals Moving Average control charts for Burr X and Inverse Gaussian distributions

2020 ◽  
Vol 30 (4) ◽  
Author(s):  
Ambreen Shafqat ◽  
Muhammad Aslam ◽  
Mohammed Albassam
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Moving Average ◽  
Inverse Gaussian ◽  
Life Test ◽  
Run Length ◽  
Truncated Life Test ◽  
Average Control ◽  
Better Than

The Burr X and Inverse Gaussian (IG) distributions are considered in this paper to design an attribute control chart for time truncated life test with Moving Average (MA) scheme w. The presentation of the MA control chart is estimated in terms of average run length (ARL) by using the Monte Carlo simulation. The ARL is decided for different values of sample sizes, MA statistics size, parameters’ values, and specified average run length. The performance of this new MA attribute control chart is compared with the usual time truncated control chart for Burr X and IG distributions. The performance of a new control chart is better than the existing control chart.

scholarly journals Explicit Formula for Average Run Length of Double Moving Control Chart for INAR(1) Processes

2016 ◽  
Author(s):  
Sukanya Phant ◽  
Saowanit Sukparungsee ◽  
Yupaporn Areepong
Keyword(s):  
Control Chart ◽  
Count Data ◽  
Average Run Length ◽  
Moving Average ◽  
Serial Dependence ◽  
Explicit Formulas ◽  
Run Length ◽  
Marginal Process ◽  
Average Control ◽  
Better Than

Count data are used in many fields of practice, especially Poisson distribution as a popular choice for the marginal process distribution. If these counts exhibit serial dependence, a popular approach is to use a Poisson INAR(1) model to describe the autocorrelation structure of process. In this paper, the explicit formulas are proposed to evaluate performance characteristics of Double Moving Average control chart (DMA) for Integer valued autoregressive of serial dependence Poisson process. The characteristics of the control chart are frequently measured as Average Run Length (ARL) which means that the average of observations are taken before a system is signaled to be out-of-control. These proposed explicit formulas of ARL are simple and easy to implement for practitioner. The numerical results show that the DMA chart performs better than others when the magnitudes of shift are moderate and large.

Evaluation of an np control chart under truncated life test using quick switching sampling system

2017 ◽  
Vol 40 (12) ◽  
pp. 3407-3414 ◽  
Author(s):  
P Jeyadurga ◽  
S Balamurali
Keyword(s):  
Control Chart ◽  
Average Run Length ◽  
Optimal Parameters ◽  
Life Test ◽  
Sampling System ◽  
Run Length ◽  
Truncated Life Test ◽  
Control Limits

In this paper, we propose a new attribute np control chart for monitoring Weibull distributed mean life of a product using a quick switching sampling system. The proposed control chart consists of two pairs of control limits, namely normal and tightened control limits. The optimal parameters of the proposed control chart, such as coefficients of control limits and experiment termination ratio, are determined so that the average run length (ARL) is close to the target in-control ARL. The ARL is calculated for various shift constants for the corresponding determined parameters. The performance of the proposed control chart is evaluated and compared with other existing charts in terms of ARL.

scholarly journals HYBRID EXPONENTIALLY WEIGHTED MOVING AVERAGE (HEWMA) CONTROL CHART BASED ON EXPONENTIAL TYPE ESTIMATOR OF MEAN

2019 ◽  
pp. 187-198 ◽  
Author(s):  
Syed Muhammad Muslim Raza ◽  
Maqbool Hussain Sial ◽  
Muhammad Haider ◽  
Muhammad Moeen Butt
Keyword(s):  
Control Chart ◽  
Exponential Type ◽  
Control Charts ◽  
Average Run Length ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Ewma Chart ◽  
Weighted Moving Average ◽  
Better Than ◽  
Exponentially Weighted

In this paper, we have proposed a Hybrid Exponentially Weighted Moving Average (HEWMA) control chart. The proposed control chart is based on the exponential type estimator for mean using two auxiliary variables (cf. Noor-ul-Amin and Hanif, 2012). We call it an EHEWMA control chart because it is based on the exponential estimator of the mean. From this study, the fact is revealed that E-HEWMA control chart shows more efficient results as compared to traditional/simple EWMA chart and DS.EWMA control chart (cf. Raza and Butt, 2018). The comparison of the E-HEWMA control chart is also performed with the DS-EWMA chart. The proposed chart also outperforms the other control chartsin comparison. The E-HEWMA chart can be used for efficient monitoring of the production process in manufacturing industries.A simulated example has been used to compare the proposed and traditional/simple EWMA charts and DS.EWMA control chart. The control charts' performance is measured using the average run length-out of control (ARL1). It is observed that the proposed chart performs better than existing EWMA control charts.  

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