Muhammad Imran¹, Jinsheng Sun^1,*, Fatima Sehar Zaidi², Zameer Abbas^3,4, Hafiz Zafar Nazir⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1207-1257, 2023, DOI:10.32604/cmes.2023.025492

Abstract Control charts (CCs) are one of the main tools in Statistical Process Control that have been widely adopted in manufacturing sectors as an effective strategy for malfunction detection throughout the previous decades. Measurement errors (M.E’s) are involved in the quality characteristic of interest, which can effect the CC’s performance. The authors explored the impact of a linear model with additive covariate M.E on the multivariate cumulative sum (CUSUM) CC for a specific kind of data known as compositional data (CoDa). The average run length is used to assess the performance of the proposed chart. The results indicate that M.E’s significantly… More >

Ibrahim M. Almanjahie^1,2, Zahid Rasheed^3,4,*, Majid Khan⁵, Syed Masroor Anwar⁶, Ammara Nawaz Cheema⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2091-2118, 2023, DOI:10.32604/cmes.2023.022201

Abstract Nonparametric (distribution-free) control charts have been introduced in recent years when quality characteristics do not follow a specific distribution. When the sample selection is prohibitively expensive, we prefer ranked-set sampling over simple random sampling because ranked set sampling-based control charts outperform simple random sampling-based control charts. In this study, we proposed a nonparametric homogeneously weighted moving average based on the Wilcoxon signed-rank test with ranked set sampling () control chart for detecting shifts in the process location of a continuous and symmetric distribution. Monte Carlo simulations are used to obtain the run length characteristics to evaluate the performance of the… More >

Korakoch Silpakob¹, Yupaporn Areepong^1,*, Saowanit Sukparungsee¹, Rapin Sunthornwat²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 281-298, 2023, DOI:10.32604/iasc.2023.032487

Abstract Control charts are one of the tools in statistical process control widely used for monitoring, measuring, controlling, improving the quality, and detecting problems in processes in various fields. The average run length (ARL) can be used to determine the efficacy of a control chart. In this study, we develop a new modified exponentially weighted moving average (EWMA) control chart and derive explicit formulas for both one and the two-sided ARLs for a p-order autoregressive (AR(p)) process with exponential white noise on the new modified EWMA control chart. The accuracy of the explicit formulas was compared to that of the well-known… More >

Sakthiseswari Ganasan¹, You Huay Woon^2,*, Zainol Mustafa¹, Dadasaheb G. Godase³

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1-10, 2023, DOI:10.32604/iasc.2023.032062

Abstract A memory-type control chart utilizes previous information for chart construction. An example of a memory-type chart is an exponentially-weighted moving average (EWMA) control chart. The EWMA control chart is well-known and widely employed by practitioners for monitoring small and moderate process mean shifts. Meanwhile, the EWMA median chart is robust against outliers. In light of this, the economic model of the EWMA and EWMA median control charts are commonly considered. This study aims to investigate the effect of cost parameters on the out-of-control average run length in implementing EWMA and EWMA median control charts. The economic model was used to… More >

Jinfu Wang¹, Shunyi Zhao^1,*, Fei Liu¹, Zhenyi Ma²

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.2, pp. 531-552, 2022, DOI:10.32604/cmes.2022.019521

Abstract In modern industry, process monitoring plays a significant role in improving the quality of process conduct. With the higher dimensional of the industrial data, the monitoring methods based on the latent variables have been widely applied in order to decrease the wasting of the industrial database. Nevertheless, these latent variables do not usually follow the Gaussian distribution and thus perform unsuitable when applying some statistics indices, especially the T2 on them. Variational AutoEncoders (VAE), an unsupervised deep learning algorithm using the hierarchy study method, has the ability to make the latent variables follow the Gaussian distribution. The partial least squares… More >

Kotchaporn Karoon, Yupaporn Areepong^*, Saowanit Sukparungsee

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 743-759, 2022, DOI:10.32604/iasc.2022.023322

Abstract Extended Exponentially Weighted Moving Average (Extended EWMA or EEWMA) control chart is one of the control charts which can quickly detect a small shift. The average run length (ARL) measures the performance of control chart. Due to the derivation of the explicit formulas for ARL on the EEWMA control chart for the autoregressive AR(p) process has not previously been reported. The aim of the article is to derive explicit formulas of ARL using a Fredholm integral equation of the second kind on EEWMA control chart for Autoregressive process, as AR(2) and AR(3) processes with exponential white noise. The accuracy of… More >

Nongnuch Saengsura, Saowanit Sukparungsee^*, Yupaporn Areepong

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 635-647, 2022, DOI:10.32604/iasc.2022.019997

Abstract In this research, we propose the new mixed control chart called the mixed Moving Average-Cumulative Sum (MA-CUSUM) control chart used for monitoring parameter changes in asymmetrical and symmetrical processes. Its efficiency was compared with that of the Shewhart, Cumulative Sum (CUSUM), Moving Average (MA), mixed Cumulative Sum-Moving Average (CUSUM-MA) and mixed Moving Average-Cumulative Sum (MA-CUSUM) control charts by using their average run lengths (ARLs), the standard deviation of the run length (SDRL), and median run length (MRL) via the Monte Carlo simulation (MC). The simulation results show that the MA-CUSUM control chart was more efficient than the other control charts… More >

Ambreen Shafqat^1,2, Muhammad Aslam^3,*, Muhammad Saleem⁴, Zameer Abbas⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 373-391, 2021, DOI:10.32604/cmes.2021.016772

Abstract The concept of neutrosophic statistics is applied to propose two monitoring schemes which are an improvement of the neutrosophic exponentially weighted moving average (NEWMA) chart. In this study, two control charts are designed under the uncertain environment or neutrosophic statistical interval system, when all observations are undermined, imprecise or fuzzy. These are termed neutrosophic double and triple exponentially weighted moving average (NDEWMA and NTEWMA) control charts. For the proficiency of the proposed chart, Monte Carlo simulations are used to calculate the run-length characteristics (such as average run length (ARL), standard deviation of the run length (SDRL), percentiles (P₂₅, P₅₀, P₇₅))… More >

Azam Zaka¹, Riffat Jabeen^2,*, Kanwal Iqbal Khan³

Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 1007-1018, 2021, DOI:10.32604/iasc.2021.016491

Abstract The weighted distributions are useful when the sampling is done using an unequal probability of the sampling units. The Weighted Power function distribution (WPFD) has applications in the fields of reliability engineering, management sciences and survival analysis. WPFD is more beneficial in Statistical process control (SPC). SPC is defined as the use of statistical techniques to control a process or production method. SPC tools and procedures can help to monitor process behaviour, discover problems in internal systems, and find solutions for production issues. To identify and remove the variation in different reliability processes and also to monitor the reliability of… More >

Azam Zaka¹, Ahmad Saeed Akhter¹, Riffat Jabeen^2,*, Aamir Sanaullah²

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1201-1223, 2021, DOI:10.32604/cmes.2021.014477

Abstract In practice, the control charts for monitoring of process mean are based on the normality assumption. But the performance of the control charts is seriously affected if the process of quality characteristics departs from normality. For such situations, we have modified the already existing control charts such as Shewhart control chart, exponentially weighted moving average (EWMA) control chart and hybrid exponentially weighted moving average (HEWMA) control chart by assuming that the distribution of underlying process follows Power function distribution (PFD). By considering the situation that the parameters of PFD are unknown, we estimate them by using three classical estimation methods,… More >