The properties of the geometric-Poisson exponentially weighted moving control chart with estimated parameters

Saghir, Aamir; Lin, Zhiwei; Chen, Ching-Wen

doi:10.1080/23311835.2014.992381

Cited by 8 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Over the period of time, EWMA is designed for process mean monitoring by [3][4][5] and for process variation monitoring by [6][7][8]. Saghir et al [9] investigated how the estimates affected the geometric-poisson EWMA chart used to monitor the number of defects in the process. EWMA and double-exponentially-weighted-moving-average (DEWMA) were investigated in [10] for the censored data of type-I.…”

Section: Introductionmentioning

confidence: 99%

Developing Bayesian EWMA chart for change detection in the shape parameter of Inverse Gaussian process

Javed,

Abbas,

Abbas

2024

PLoS ONE

View full text Add to dashboard Cite

Bayesian Control charts are emerging as the most efficient statistical tools for monitoring manufacturing processes and providing effective control over process variability. The Bayesian approach is particularly suitable for addressing parametric uncertainty in the manufacturing industry. In this study, we determine the monitoring threshold for the shape parameter of the Inverse Gaussian distribution (IGD) and design different exponentially-weighted-moving-average (EWMA) control charts based on different loss functions (LFs). The impact of hyperparameters is investigated on Bayes estimates (BEs) and posterior risks (PRs). The performance measures such as average run length (ARL), standard deviation of run length (SDRL), and median of run length (MRL) are employed to evaluate the suggested approach. The designed Bayesian charts are evaluated for different settings of smoothing constant of the EWMA chart, different sample sizes, and pre-specified false alarm rates. The simulative study demonstrates the effectiveness of the suggested Bayesian method-based EWMA charts as compared to the conventional classical setup-based EWMA charts. The proposed techniques of EWMA charts are highly efficient in detecting shifts in the shape parameter and outperform their classical counterpart in detecting faults quickly. The proposed technique is also applied to real-data case studies from the aerospace manufacturing industry. The quality characteristic of interest was selected as the monthly industrial production index of aircraft from January 1980 to December 2022. The real-data-based findings also validate the conclusions based on the simulative results.

show abstract

Section: Introductionmentioning

confidence: 99%

Developing Bayesian EWMA chart for change detection in the shape parameter of Inverse Gaussian process

Javed,

Abbas,

Abbas

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Girshick and Rubin 18 are the first who researched the notion of Bayesian CC for location parameter. Saghir et al 19 introduced a Bayesian CC that utilizes the P distribution to identify fluctuations in location parameter. Their method considers different LFs, allowing flexibility in capturing the underlying process characteristics.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of semiconductor manufacturing process on Bayesian AEWMA control chart under paired ranked set sampling schemes

Wang,

Khan,

Noor-ul-Amin

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Quality control often employs memory-type control charts, including the exponentially weighted moving average (EWMA) and Shewhart control charts, to identify shifts in the location parameter of a process. This article pioneers a new Bayesian Adaptive EWMA (AEWMA) control chart, built on diverse loss functions (LFs) such as the square error loss function (SELF) and the Linex loss function (LLF). The proposed chart aims to enhance the process of identifying small to moderate as well as significant shifts in the mean, signifying a notable advancement in the field of quality control. These are implemented utilizing an informative prior for both posterior and posterior predictive distributions, employing various paired ranked set sampling (PRSS) schemes. The effectiveness of the suggested chart is appraised using average run length (ARL) and the standard deviation of run length (SDRL). Monte Carlo simulations are employed to contrast the recommended approach against other control charts. The outcomes demonstrate the dignitary performance of the recommended chart in identifying out-of-control signals, especially applying PRSS designs, in comparison to simple random sampling (SRS). Finally, a practical application was conducted in the semiconductor manufacturing context to appraise the efficacy of the offered chart using various paired ranked set sampling strategies. The results reveal that the suggested control chart performed well in capturing the out-of-control signals far better than the already in use control charts. Overall, this study interposes a new technique with diverse LFs and PRSS designs, improving the precision and effectiveness in detecting process mean shifts, thereby contributing to advancements in quality control and process monitoring.

show abstract

“…In the recent literature, the LFs are studied in statistical quality control. For the large shift, Riaz and Ali 3 1 introduced the Shehwart control chart under different LFs. Riaz et al 3 2 proposed the Bayesian EWMA control chart by using informative and noninformative prior under three different LFs for posterior distribution and posterior predictive distribution.…”

Section: Introductionmentioning

confidence: 99%

An adaptive EWMA control chart for monitoring the process mean in Bayesian theory under different loss functions

Noor‐ul‐Amin

Noor

2020

Quality & Reliability Eng

View full text Add to dashboard Cite

The Shewhart control chart is used for detecting the large shift and an exponentially weighted moving average (EWMA) control chart is used for detecting the small/moderate shift in the process mean. A scheme that combines both the Shewhart control chart and the EWMA control chart in a smooth way is called the adaptive EWMA (AEWMA) control chart. In this paper, we proposed a new AEWMA control chart for monitoring the process mean in Bayesian theory under different loss functions (LFs). We used informative (conjugate prior) under two different LFs: (1) squared error loss function and (2) linex loss function for posterior and posterior predictive distributions. We used the average run length and standard deviation of run length to measure the performance of the AEWMA control chart in the Bayesian theory. A comparative study is conducted for comparing the proposed AEWMA control chart in Bayesian theory with the existing Bayesian EWMA control chart. We conducted a Monte Carlo simulation study to evaluate the proposed AEWMA control chart. For the implementation purposes, we presented a real‐data example.

show abstract

The properties of the geometric-Poisson exponentially weighted moving control chart with estimated parameters

Cited by 8 publications

References 22 publications

Developing Bayesian EWMA chart for change detection in the shape parameter of Inverse Gaussian process

Developing Bayesian EWMA chart for change detection in the shape parameter of Inverse Gaussian process

Monitoring of semiconductor manufacturing process on Bayesian AEWMA control chart under paired ranked set sampling schemes

An adaptive EWMA control chart for monitoring the process mean in Bayesian theory under different loss functions

Contact Info

Product

Resources

About