Detecting a Shift in Fraction Nonconforming Using Run-Length Control Charts with 100% Inspection

Bourke, Patrick D.

doi:10.1080/00224065.1991.11979328

Cited by 248 publications

(177 citation statements)

References 11 publications

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“…The conforming run length (CRL) chart is proposed by Bourke (1991). The Conforming run length is the number of inspected units between two consecutive nonconforming units including ending nonconforming unit.…”

Section: The Conforming Run Length Control Chartmentioning

confidence: 99%

Steady-state behavior of nonparametric control charts using sign statistic

Khilare¹,

Shirke

2015

Prod.

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If process is running for a long period in an in-control condition, it will reach in a steady-state condition. In order to study the long term properties of a control chart, it is appropriate to investigate the steady-state average time to signal. In this article, we discussed runs rules representation of a nonparametric synthetic control chart using sign statistic for detecting shifts in location parameter. We compared zero-state average time to signal with steady-state average time to signal of the synthetic control chart for symmetric and asymmetric distributions. We also present the m-of-m control chart using sign statistic. For comparison study, we computed average time to signal of the m-of-m control chart, the sign chart (1-of-1 chart) and the synthetic control chart for normal, Cauchy, double exponential and gamma distributions. Steady-state and zero-state performance of the m-of-m control chart with m = 2, 3 compared with the sign chart (1-of-1 chart) and synthetic control chart. The zero-state and steady-state average time to signal of the synthetic and the m-of-m control charts computed using Markov chain approach.

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Section: The Conforming Run Length Control Chartmentioning

confidence: 99%

Steady-state behavior of nonparametric control charts using sign statistic

Khilare¹,

Shirke

2015

Prod.

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“…Several multivariate control charts have been proposed (Bourke, 1991;El-Midany et al, 2010;Montgomery, 2013;Xie and Goh, 1992) and presented the copula approach that use to make the joint distribution of two data series; however, few investigations have been reported on copula modelling for a multivariate control chart. This paper is organized as: Section 2 contains some MSPC.…”

Section: Introductionmentioning

confidence: 99%

Copula modelling for multivariate statistical process control: a review

Busababodhin

Amphanthong

2016

CSAM

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Modern processes often monitor more than one quality characteristic that are referred to as multivariate statistical process control (MSPC) procedures. The MSPC is the most rapidly developing sector of statistical process control and increases interest in the simultaneous inspection of several related quality characteristics. Most multivariate detection procedures based on a multi-normality assumptions are independent, but there are many processes that assume non-normality and correlation. Many multivariate control charts have a lack of related joint distribution. Copulas are tool to construct multivariate modelling and formalizing the dependence structure between random variables and applied in several fields. From copula literature review, there are a few copula to apply in MSPC that have multivariate control charts, and represent a successful tool to identify an out-of-control process. This paper presents various types of copulas modelling for the multivariate control chart. The performance measures of the control chart are the average run length (ARL) and the average number of observations to signal (ANOS). Furthermore, a Monte Carlo simulation is shown when the observations were from an exponential distribution.

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“…The conforming run length (CRL) chart was first introduced by [1]. The CRL chart is an attributes chart to detect shifts in the fraction non-conforming, p. The CRL chart determines whether a process is in-control or not, based on the number of conforming units between two non-conforming units.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Run Length Distribution of Synthetic X Chart

Chong¹,

Khoo²,

You³

2016

IJET

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Abstract-Quality is a vital element in every field. In order to compete among the competitors, the manufacturer or service provider should produce or provide quality product or service. However, before a product or service can be considered as having an acceptable quality level, all the processes producing the product (or providing the service) should be in-control. The synthetic X chart consists of two sub-charts, i.e. the X /S sub-chart and CRL/S sub-chart. The synthetic X chart is used to detect shifts in the process mean. In this paper, we study the percentage points (percentiles) of the run length distribution of the synthetic X chart so as to provide practitioners with a more complete understanding of the behaviour of the chart, instead of the sole dependence on the average run length (ARL).Index Terms-Average run length (ARL), conforming run length (CRL), run length distribution, synthetic X chart.

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Detecting a Shift in Fraction Nonconforming Using Run-Length Control Charts with 100% Inspection

Cited by 248 publications

References 11 publications

Steady-state behavior of nonparametric control charts using sign statistic

Steady-state behavior of nonparametric control charts using sign statistic

Copula modelling for multivariate statistical process control: a review

A Study on the Run Length Distribution of Synthetic X Chart

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