The performance of the hypergeometric <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mrow><mml:mi>n</mml:mi><mml:mi>p</mml:mi></mml:mrow></mml:math> chart with estimated parameter

Johannssen, Arne; Chukhrova, Nataliya; Castagliola, Philippe

doi:10.1016/j.ejor.2021.06.056

Cited by 17 publications

(2 citation statements)

References 43 publications

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np

charts based on the hypergeometric distribution have been proposed to account for the lot size effect in periodical processes, but without taking into account the characteristics of FHP processes, see Chukhrova & Johannssen 44,45 and Johannssen et al 46 …”

Section: Introductionmentioning

confidence: 99%

“…In this scenario, a correct investigation of attribute control charts for monitoring the fraction nonconforming in a FHP process calls for the definition of a sound statistical theory allowing for the flexibility of monitoring both low-volume and high-volume production. Recently, 𝑝 and 𝑛𝑝 charts based on the hypergeometric distribution have been proposed to account for the lot size effect in periodical processes, but without taking into account the characteristics of FHP processes, see Chukhrova & Johannssen 44,45 and Johannssen et al 46 . However, efficient monitoring of FHP processes requires an adequate embedding of their characteristics in the modeling and thus a specific control chart design and different performance measures than for monitoring processes that do not have a finite horizon.…”

Section: Introductionmentioning

confidence: 99%

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A number‐between‐events control chart for monitoring finite horizon production processes

Johannssen

Chukhrova

Celano

et al. 2022

Quality & Reliability Eng

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In this paper, we present a number‐between‐events (NBE) control chart for monitoring the fraction nonconforming in finite horizon production (FHP) processes and related specific performance measures. When monitoring fractions nonconforming in FHP processes, the common binomial p$p$‐chart has two crucial limitations: the underlying distributional assumptions are violated when dealing with low‐volume production and a scarce efficiency in the case of processes characterized by a low fraction nonconforming. Thus, an efficient monitoring of FHP processes requires the selection of the correct underlying statistical model: in this case, a distribution from the hypergeometric family of discrete statistical distributions. An efficient statistical monitoring of processes with low fractions nonconforming can be achieved by means of discrete time‐between‐events (TBE) control charts, which count the number of units up to the appearance of a fixed number of nonconforming units in the sample. Here, we present a discrete TBE‐chart, denoted as NBE‐chart, based on the negative hypergeometric distribution that meets numerous requirements of efficient monitoring of FHP processes. The proposed control chart can be conveniently used for both low‐volume and mass production in processes with frequent changeovers.

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p

and

np

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A number‐between‐events control chart for monitoring finite horizon production processes

Johannssen

Chukhrova

Celano

et al. 2022

Quality & Reliability Eng

Self Cite

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A Study on the Performances of the SPRT Control Chart When Estimating Process Parameters

Teoh

El-Ghandour

et al. 2023

Lecture Notes in Electrical Engineering

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Employing machine learning techniques in monitoring autocorrelated profiles

Yeganeh

Johannssen

Chukhrova

et al. 2023

Neural Comput & Applic

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In profile monitoring, it is usually assumed that the observations between or within each profile are independent of each other. However, this assumption is often violated in manufacturing practice, and it is of utmost importance to carefully consider autocorrelation effects in the underlying models for profile monitoring. For this reason, various statistical control charts have been proposed to monitor profiles when between- or within-data is correlated in Phase II, in which the main aim is to develop control charts with quicker detection ability. As a novel approach, this study aims to employ machine learning techniques as control charts instead of statistical approaches in monitoring profiles with between-profile autocorrelations. Specifically, new input features based on conventional statistical control chart statistics and normalized estimated parameters are defined that are capable of adequately accounting for the between-autocorrelation effect of profiles. In addition, six machine learning techniques are extended and compared by means of Monte Carlo simulations. The simulation results indicate that machine learning techniques can obtain more accurate results compared with statistical control charts. Moreover, adaptive neuro-fuzzy inference systems outperform other machine learning techniques and the conventional statistical control charts.

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The performance of the hypergeometric np chart with estimated parameter

Cited by 17 publications

References 43 publications

A number‐between‐events control chart for monitoring finite horizon production processes

A number‐between‐events control chart for monitoring finite horizon production processes

A Study on the Performances of the SPRT Control Chart When Estimating Process Parameters

Employing machine learning techniques in monitoring autocorrelated profiles

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