A shewhart‐like charting technique for high yield processes

Xie, Wei; Xie, Min; Goh, T. N.

doi:10.1002/qre.4680110309

Cited by 31 publications

(21 citation statements)

References 9 publications

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“…Control limits were initially set by Calvin and Goh using approximations on the basis of the geometric distribution. The control limits most commonly used for the CCC chart are derived from the cumulative distribution function (cdf) of Y , that is,

F (y) = 1 - {((), 1 - p)}^{y},

for y = 1,2,… Bourke and Xie et al set the control limits for an in‐control value of p 0 based on the probability limits, where F (LCL) ≈ α /2 and F (UCL) ≈ 1 − α /2 to obtain an overall false alarm rate close to α , with roughly equal tail probabilities. These control limits are better to use than the symmetric ‘3–sigma’ limits for the CCC chart because the geometric distribution is quite skewed.…”

Section: Shewhart Control Charts Based On the Geometric Distributionmentioning

confidence: 99%

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A Review and perspective on surveillance of Bernoulli processes

Szarka

Woodall

2011

Quality & Reliability Eng

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Bernoulli processes have been monitored using a wide variety of techniques in statistical process control. The data consist of information on successive items classified as conforming (nondefective) or nonconforming (defective). In some cases, the probability of obtaining a nonconforming item is very small; this is known as a high quality process. This area of statistical process control is also applied to health-related monitoring, where the incidence rate of a medical problem such as a congenital malformation is of interest. In these applications, standard Shewhart control charts based on the binomial distribution are no longer useful. In our expository paper, we review the methods implemented for these scenarios and present ideas for future work in this area. We offer advice to practitioners and present a comprehensive literature review for researchers.

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F (y) = 1 - {((), 1 - p)}^{y},

Section: Shewhart Control Charts Based On the Geometric Distributionmentioning

confidence: 99%

“…In this context, process deterioration may not be detected. The authors recommended using probability limits similar to what was proposed by Xie et al for the CCC chart. The technical issues with the g ‐chart were also discussed by Xie et al …”

Section: Control Charts Based On the Negative Binomial Distributionmentioning

confidence: 99%

A Review and perspective on surveillance of Bernoulli processes

Szarka

Woodall

2011

Quality & Reliability Eng

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“…This chart is also known as cumulative count of conforming (CCC) chart. Xie et al (1995) argued that the original layout of Calvin's chart is difficult to interpret and provided an improved version of the chart layout. Kuralmani et al (2002) proposed a conditional CCC chart which is similar to the CCC chart.…”

Section: Review Of Control Charts For High Yield Processesmentioning

confidence: 99%

Modified Shewhart Charts for High Yield Processes

Chang¹,

Gan

2007

Journal of Applied Statistics

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The conventional Shewhart p or np chart is not effective for monitoring a high yield process, a process in which the defect level is close to zero. An improved Shewhart np chart for monitoring high yield processes is proposed. A review of control charts for monitoring high yield processes is first given. The run length performance of the proposed Shewhart chart is then compared with other high yield control charts. A simple procedure for designing the chart for processes subjected to sampling or 100% continuous inspection is provided and this allows the chart to be implemented easily on the factory floor. The practical aspects of implementation of the Shewhart chart are discussed. An application of the Shewhart chart based on a real data set is demonstrated.Average run length, binomial counts, parts-per-million non-conforming items, supplementary runs rules, statistical process control,

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“…The CRL follows a geometric distribution with cdf [41] where p is the probability of a non-conforming TBE, X on the T/S sub-chart, such thatSince the detection of an increase in p is the only concern, the lower control limit of the CRL/S sub-chart, (rounded to an integer) is sufficient [16], [41] …”

Section: Literature Review: a Review On Time-between-events Control Cmentioning

confidence: 99%

“…Since then, the conforming run length (CRL) control chart was proposed and has been studied for better interpretation purposes [16], [17]. The CRL chart is also known as the geometric chart [18].…”

Section: Introductionmentioning

confidence: 99%

Synthetic-Type Control Charts for Time-Between-Events Monitoring

2013

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This paper proposes three synthetic-type control charts to monitor the mean time-between-events of a homogenous Poisson process. The first proposed chart combines an Erlang (cumulative time between events, Tr) chart and a conforming run length (CRL) chart, denoted as Synth-Tr chart. The second proposed chart combines an exponential (or T) chart and a group conforming run length (GCRL) chart, denoted as GR-T chart. The third proposed chart combines an Erlang chart and a GCRL chart, denoted as GR-Tr chart. By using a Markov chain approach, the zero- and steady-state average number of observations to signal (ANOS) of the proposed charts are obtained, in order to evaluate the performance of the three charts. The optimal design of the proposed charts is shown in this paper. The proposed charts are superior to the existing T chart, Tr chart, and Synth-T chart. As compared to the EWMA-T chart, the GR-T chart performs better in detecting large shifts, in terms of the zero- and steady-state performances. The zero-state Synth-T4 and GR-Tr (r = 3 or 4) charts outperform the EWMA-T chart for all shifts, whereas the Synth-Tr (r = 2 or 3) and GR-T 2 charts perform better for moderate to large shifts. For the steady-state process, the Synth-Tr and GR-Tr charts are more efficient than the EWMA-T chart in detecting small to moderate shifts.

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A shewhart‐like charting technique for high yield processes

Cited by 31 publications

References 9 publications

A Review and perspective on surveillance of Bernoulli processes

A Review and perspective on surveillance of Bernoulli processes

Modified Shewhart Charts for High Yield Processes

Synthetic-Type Control Charts for Time-Between-Events Monitoring

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