A variable parameters auxiliary information based quality control chart with application in a spring manufacturing process: The Markov chain approach

Chong, Nger Ling; Khoo, Michael B. C.; Castagliola, Philippe; Saha, Sajal; Mim, Faijun Nahar

doi:10.1080/08982112.2020.1830417

Cited by 4 publications

(2 citation statements)

References 42 publications

(60 reference statements)

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“…As one of the most important tools in statistical process monitoring (SPM), control charts have been extensively used in various fields to monitor possible deteriorations of processes, for instance, chemical and process industries, natural disaster monitoring, or healthcare. Readers can refer to Anwar et al (2020), Perry (2020), Zhou et al (2020), and Chong et al (2020) for some recent research works on the application of control charts. Among the traditional control charts, Shewhart-type ones received much attention because they are easy to implement and very effective for monitoring large shifts.…”

Section: Introductionmentioning

confidence: 99%

One-sided Adaptive Truncated Exponentially Weighted Moving Average X¯ Schemes for Detecting Process Mean Shifts

Xie

Castagliola

et al. 2022

Quality Technology & Quantitative Management

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One-sided type schemes are known to be more appropriate for monitoring a process when the direction of a potential mean shift can be anticipated. Furthermore, if the magnitude of the potential mean shift is unknown, it is desired to design a control chart to perform well over a wide range of shifts instead of only optimizing its performance in monitoring a particular mean shift level. The one-sided adaptive truncated exponentially weighted moving average (ATEWMA) X scheme recommended in this paper is a control chart that combines a Shewhart X scheme and a new one-sided EWMA X scheme together in a smooth way for rapidly detecting the upward (or downward) mean shifts. The basic idea of the recommended one-sided ATEWMA X scheme is to truncate the observations (i.e., the sample means X) first, and then to dynamically weight the past observations according to a suitable function of the current prediction error. This helps to improve the sensitivity of the proposed one-sided ATEWMA X scheme for detecting both small and

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Section: Introductionmentioning

confidence: 99%

One-sided Adaptive Truncated Exponentially Weighted Moving Average X¯ Schemes for Detecting Process Mean Shifts

Xie

Castagliola

et al. 2022

Quality Technology & Quantitative Management

Self Cite

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show abstract

“…As one of the most influential tools in statistical process monitoring (SPM), control charts have been extensively used in various fields, for example, manufacturing industry (see Chong et al (2021)) and network monitoring (see Perry (2020)). According to Castagliola et al (2019), in SPM, there are two categories of control charts: the memoryless-type charts (for instance, the Shewhart charts) and the memory-type charts (such as the exponentially weighted moving average (EWMA) charts).…”

Section: Introductionmentioning

confidence: 99%