A new nonparametric adaptive EWMA control chart with exact run length properties

Tang, Anan; Sun, Jian; Hu, Xuelong; Castagliola, Philippe

doi:10.1016/j.cie.2019.02.045

Cited by 38 publications

(36 citation statements)

References 35 publications

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“…Haq 15 developed the nonparametric synthetic EWMA chart, and Castagliola et al 16 proposed a Phase II EWMA-SN chart that uses integer-valued weights. Other works on nonparametric Shewhart-type, EWMA, and CUSUM control charts are those of Graham et al, 17 Li et al, 18 Yang and Chen, 19 Wang et al, 20 Haq, 21 Abid et al, 22 Koutras and Triantafyllou, 23 and Tang et al 24 An extensive analysis of nonparametric control charts can be seen in Chakraborti and Graham 4 while an updated overview in the work of Chakraborti and Graham. 25 To enhance the performance of nonparametric EWMA charts in detecting especially small process shifts, other memory-types charts have been used.…”

Section: Introductionmentioning

confidence: 99%

A nonparametric double generally weighted moving average signed‐rank control chart for monitoring process location

Alevizakos

Koukouvinos

Chatterjee

2020

Quality & Reliability Eng

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Most control charts have been developed based on the actual distribution of the quality characteristic of interest. However, in many applications, there is a lack of knowledge about the process distribution. Therefore, in recent years, nonparametric (or distribution-free) control charts have been introduced for monitoring the process location or scale parameter. In this article, a nonparametric double generally weighted moving average control chart based on the signed-rank statistic (referred as DGWMA-SR chart) is proposed for monitoring the location parameter. We provide the exact approach to compute the run-length distribution, and through an extensive simulation study, we compare the performance of the proposed chart with existing nonparametric charts, such as the exponentially weighted moving average signed-rank (EWMA-SR), the generally weighted moving average signed-rank (GWMA-SR), the double exponentially weighted moving average signed-rank (DEWMA-SR), and the double generally weighted moving average sign (DGWMA-SN) charts, as well as the parametric DGWMA-X chart for subgroup averages. The simulation results show that the DGWMA-SR chart (with suitable parameters) is more sensitive than the other competing charts for small shifts in the location parameter and performs as well as the other nonparametric charts for larger shifts. Finally, two examples are given to illustrate the application of the proposed chart.

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Section: Introductionmentioning

confidence: 99%

A nonparametric double generally weighted moving average signed‐rank control chart for monitoring process location

Alevizakos

Koukouvinos

Chatterjee

2020

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…Various approaches have been proposed in the literature to monitor nonnormal distributed data. Among them, some nonparametric control charts have been proposed to monitor univariate and multivariate skewed populations, see the literature 13‐16 . Although attractive, these nonparametric control charts are difficult to apply in practice due to the expensive computations required for their implementation.…”

Section: Introductionmentioning

confidence: 99%

“…Among them, some nonparametric control charts have been proposed to monitor univariate and multivariate skewed populations, see the literature. [13][14][15][16] Although attractive, these nonparametric control charts are difficult to apply in practice due to the expensive computations required for their implementation. On the other hand, with the help of some data transformations, a multivariate nonnormal distribution can be approximately transformed into a multivariate normal distribution or into the combination of different normal distributions, for example, using the double square-root method (see Kittlitz 17 ) or the weighted standard deviation method (see Chang 18,19 ).…”

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confidence: 99%

A multivariate CUSUM control chart for monitoring Gumbel's bivariate exponential data

Xie

Sun

Castagliola

et al. 2020

Quality & Reliability Eng

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Exponentially distributed data are commonly encountered in high‐quality processes. Control charts dedicated to the univariate exponential distribution have been extensively studied by many researchers. In this paper, we investigate a multivariate cumulative sum (MCUSUM) control chart for monitoring Gumbel's bivariate exponential (GBE) data. Some tables are provided to determine the optimal design parameters of the proposed MCUSUM GBE chart. Furthermore, both zero‐state and steady‐state properties of the proposed MCUSUM GBE chart for the raw and the transformed GBE data are compared with the multivariate exponentially weighted moving average (MEWMA) chart and the paired individual cumulative sum (CUSUM) chart. The results show that the proposed MCUSUM GBE chart outperforms the other two types of control charts for most shift domains. In addition, an extension to Gumbel's multivariate exponential (GME) distribution is also investigated. Finally, an illustrative example is provided in order to explain how the proposed MCUSUM GBE chart can be implemented in practice.

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“…In these papers, inverse ranks (or anti‐ranks) were used. Moreover, some Shewhart‐type nonparametric charts are proposed by Bakir [2] and Chakraborti et al [5]; some cumulative sum (CUSUM)‐type nonparametric charts are proposed by McDonald [18], and Albers and Kallenberg [1]; and some exponentially weighted moving average (EWMA)‐type nonparametric charts are proposed by Hackl and Ledolter [6], Zou and Tsung [33], and Tang et al [27]. Other works on nonparametric control charts for univariate processes include Jones‐Farmer et al [8], Qiu and Li [25], Zhou et al [33], and Chakraborti and Graham [3].…”

Section: Introductionmentioning

confidence: 99%

An adaptive nonparametric exponentially weighted moving average control chart with dynamic sampling intervals

Liu

Peng

Lai

et al. 2020

Statistical Analysis

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Nonparametric statistical control charts have been widely studied over the past decade. The variable sampling intervals (VSIs) methods have been found to exhibit superior performance compared to the traditional fixed sampling policy in this area. In previous VSI studies, the sampling interval function d(⋅) realized only two distinct values. Limiting the selection of the sampling interval to two fixed values may not be reasonable, especially when the monitoring statistic value is at the edge or near the middle of these two intervals so that the advantages of VSI methods cannot be fully reflected. Therefore, we propose a nonparametric exponentially weighed moving average control chart with a dynamic sampling interval in this article, in which the dynamic sampling interval is an extension of two fixed sampling intervals. The dynamic sampling interval can be continuous valued and is determined by an indicator of the magnitude of the unknown shift. This chart can efficiently detect various magnitudes of shifts and performs robustly for different distributions according to simulation studies. The small computational cost indicates that the method is applicable for monitoring large data streams. Data from an aluminum electrolytic capacitor manufacturing process are used to illustrate the implementation of this chart.

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A new nonparametric adaptive EWMA control chart with exact run length properties

Cited by 38 publications

References 35 publications

A nonparametric double generally weighted moving average signed‐rank control chart for monitoring process location

A nonparametric double generally weighted moving average signed‐rank control chart for monitoring process location

A multivariate CUSUM control chart for monitoring Gumbel's bivariate exponential data

An adaptive nonparametric exponentially weighted moving average control chart with dynamic sampling intervals

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