Double exponentially weighted moving average control chart with supplementary runs-rules

Adeoti, Olatunde Adebayo; Malela‐Majika, Jean‐Claude

doi:10.1080/16843703.2018.1560603

Cited by 45 publications

(23 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The DEWMA statistic is a weighted combination of the current and past observation by performing exponential smoothing twice (cf. Adeoti and Malela‐Majika 16 ). The DEWMA statistic Z i is defined as

Y_{i} = λ {\overset{false¯}{X}}_{i} + ((), 1 - λ) Y_{i - 1},

Z_{i} = λ Y_{i} + ((), 1 - λ) Z_{i - 1},

where

{\overset{false¯}{X}}_{i}

is the sample mean of the i th subgroup and Y 0 = Z 0 = μ 0 .…”

Section: Brief Review Of Some Existing Control Chartsmentioning

confidence: 99%

“…In a bid to further increase the sensitivity of the control charts to small, moderate and large shifts, several design strategies and modifications of the control charts have been introduced by researchers, which have led to the development of new control charts. For some relevant papers on these new control charts, readers are referred to Capizzi and Masaroto, 4 Al‐Shabah, 5 Abbas et al 6 Graham et al 7 Abujiya et al 8 Abbas et al 9 Lu, 10 Sanusi et al 11 Aldosari et al 12 Haq, 13 Haq et al 14 Nawaz et al 15 and Adeoti and Malela‐Majika 16 . All of these control charts, as in the case of many papers, are based on the assumption that process parameters are known.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid homogeneously weighted moving average control chart for process monitoring

Adeoti

Koleoso

2020

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

Several modifications and enhancements to control charts in increasing the performance of small and moderate process shifts have been introduced in the quality control charting techniques. In this paper, a new hybrid control chart for monitoring process location is proposed by combining two homogeneously weighted moving average (HWMA) control charts. The hybrid homogeneously weighted moving average (HHWMA) statistic is derived using two smoothing constants λ 1 and λ 2. The average run length (ARL) and the standard deviation of the run length (SDRL) values of the HHWMA control chart are obtained and compared with some existing control charts for monitoring small and moderate shifts in the process location. The results of study show that the HHWMA control chart outperforms the existing control charts in many situations. The application of the HHWMA chart is demonstrated using a simulated data. K E Y W O R D S average run length (ARL), control chart, homogeneously weighted moving average (HWMA), hybrid homogeneously weighted moving average (HHWMA), process monitoring

show abstract

Y_{i} = λ {\overset{false¯}{X}}_{i} + ((), 1 - λ) Y_{i - 1},

Z_{i} = λ Y_{i} + ((), 1 - λ) Z_{i - 1},

where

{\overset{false¯}{X}}_{i}

is the sample mean of the i th subgroup and Y 0 = Z 0 = μ 0 .…”

Section: Brief Review Of Some Existing Control Chartsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hybrid homogeneously weighted moving average control chart for process monitoring

Adeoti

Koleoso

2020

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…Reference [7] presented an EWMA chart for monitoring the variation in the process. The application of EWMA-based control chart can be seen in References [8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Design of S2N—NEWMA Control Chart for Monitoring Process having Indeterminate Production Data

2019

View full text Add to dashboard Cite

The existing charts for monitoring the variance are designed under the assumption that all production data must consist of exact, precise, and determined observations. This paper presents the design of a new neutrosophic exponentially weighted moving average (NEWMA) combining with a neutrosophic logarithmic transformation chart for monitoring the variance having neutrosophic numbers. The computation of the neutrosophic control chart parameters is done through the neutrosophic Monte Carlo simulation (NMCS). The performance of the proposed chart is discussed with the existing charts.

show abstract

“…The chart detects smaller shifts more quickly than the classical CUSUM, classical EWMA, and MEC control charts but it has a limitation that the variance expression of the hybrid EWMA statistic becomes undefined when the smoothing constants λ 1 and λ 2 are equal. Recently, Adeoti and Malela-Majika 7 introduced different run rules schemes in double exponentially weighted moving average (DEWMA) to enhance its efficiency further for small to moderate shifts in the process mean.…”

Section: Introductionmentioning

confidence: 99%

A new nonparametric double exponentially weighted moving average control chart

Raza

Nawaz

Aslam

et al. 2019

Quality & Reliability Eng

View full text Add to dashboard Cite

There are many practical situations where the underlying distribution of the quality characteristic either deviates from normality or it is unknown. In such cases, practitioners often make use of the nonparametric control charts. In this paper, a new nonparametric double exponentially weighted moving average control chart on the basis of the signed-rank statistic is proposed for monitoring the process location. Monte Carlo simulations are carried out to obtain the run length characteristics of the proposed chart. The performance comparison of the proposed chart with the existing parametric and nonparametric control charts is made by using various performance metrics of the run length distribution. The comparison showed the superiority of the suggested chart over its existing parametric and nonparametric counterparts. An illustrative example for the practical implementation of the proposed chart is also provided by using the industrial data set.

show abstract

Double exponentially weighted moving average control chart with supplementary runs-rules

Cited by 45 publications

References 31 publications

A hybrid homogeneously weighted moving average control chart for process monitoring

A hybrid homogeneously weighted moving average control chart for process monitoring

Design of S2N—NEWMA Control Chart for Monitoring Process having Indeterminate Production Data

A new nonparametric double exponentially weighted moving average control chart

Contact Info

Product

Resources

About