Nonparametric control chart based on change-point model

Zhou, Chunguang; Zou, Changliang; Zhang, Yujuan; Wang, Zhaojun

doi:10.1007/s00362-007-0054-7

Cited by 88 publications

(49 citation statements)

References 32 publications

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“…To deal with non-Normal data in short-run processes, Zhou et al [46] developed a nonparametric control chart inspired in the change-point model to control the parameter of location. Zou and Tsung [47] provided a solution to monitor both the location and dispersion parameters of a short-run process by employing a likelihood ratio-based distribution-free method.…”

Section: Spc For Short Production Runs -Literature Reviewmentioning

confidence: 99%

Selection of the Most Suitable Statistical Process Control Approach for Short Production Runs: A Decision-Model

Marques¹,

Cardeira²,

Paranhos³

et al. 2015

IJIET

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Abstract-Nowadays, customers are increasingly claiming not only for better quality products at the lowest possible cost, but also demanding customized solutions to satisfy their specific, sometimes unique, needs and wants. Due to this, manufacturing companies are seeking to adopt higher agile production models, such as mass customization strategies. In the quality control field, statistical process control (SPC) methods have been widely used to monitor process performance and detect abnormal situations in its behavior; however, traditional SPC approaches are usually not appropriate for small lot or batch sizes, for the start-up of a process, and for situations where a high variety of mixed products exist. Such situations are within the scope of the so called short production runs. Several SPC schemes have been proposed for short-run environments; all of them have their own advantages, shortcomings, and more suitable for certain production scenarios. This paper provides an up-to-date literature review on the topic, identifies classes of SPC short-run methods, and presents a decision-model that guides production managers in the choice of the most appropriate SPC short-run approach. The model was validated in a textile production company, and is being incorporated into a software package.Index Terms-Decision-model, short-run, statistical process control (SPC).

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Section: Spc For Short Production Runs -Literature Reviewmentioning

confidence: 99%

Selection of the Most Suitable Statistical Process Control Approach for Short Production Runs: A Decision-Model

Marques¹,

Cardeira²,

Paranhos³

et al. 2015

IJIET

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show abstract

“…They showed that, in many cases, the MW chart performs better than the competing charts no matter the nature of the underlying process distribution. Among other uses of the MW statistic, a change-point formulation was investigated by Zhou et al [4]. Li et al [5] proposed a nonparametric cumulative sum (CUSUM) chart and a nonparametric exponentially weighted moving average (EWMA) control chart based on the Wilcoxon rank-sum (WRS) statistic W, respectively, with the W statistic being equivalent to the MW statistic.…”

Section: Introductionmentioning

confidence: 99%

Distribution-free Phase II Mann–Whitney control charts with runs-rules

Malela‐Majika

Chakraborti

Graham

2015

Int J Adv Manuf Technol

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The addition of runs-rules has been recommended to improve the performance of classical, normal theory Shewhart-type control charts, for detecting small to moderate size shifts. In this paper, we consider adding both standard and improved runs-rules to enhance the performance of the distribution-free Phase II Shewhart-type chart based on the well-known Mann-Whitney statistic proposed by Chakraborti and Van de Wiel [1]. Standard runs-rules are typically of the form w-of-(w+v) with w > 1 and v 0 and the improved runs-rules scheme is a combination of the classical 1-of-1 runs-rule and the w-of-(w+v) runs-rules. The improved scheme improves the performance of the charts in detecting larger shifts while maintaining its performance in detecting small to moderate shifts. The in-control and out-of-control performance of the proposed runs-rules enhanced distribution-free charts are examined through extensive simulations. It is seen that the proposed charts have attractive performance compared to some competing charts, and are better in many cases. An illustrative example is provided, along with a summary and conclusions.

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“…And, as an extension, Zamba and Hawkins (2006) developed the idea for shift in multivariate normal data by using likelihood ratio test. However, Zhou et al (2009) extension relies on Mann-Whitney statistic for a change in mean only while Deng (2009) considered (a univariate) Wilcoxon-Mann-Whitney non-parametric test for difference in location (mean). In addition, Ross et al (2011) proposed a technique for monitoring a change in the location and/or scale parameter of an arbitrary continuously distributed univariate data stream with a single parameter shift.…”

Section: Introductionmentioning

confidence: 99%

“…Conventional charting procedures are developed under the assumption that the in-control parameters are known exactly. In real life, the assumption of known in-control parameters are usually not met (Hawkins et al, 2003;Zhou et al, 2009). Therefore, if the assumption of normality is satisfied, work has advanced in detecting shifts in mean and/or variance with unknown in-control parameters (Hawkins and Zamba, 2005b).…”

mentioning

confidence: 99%

“…Therefore, if the assumption of normality is satisfied, work has advanced in detecting shifts in mean and/or variance with unknown in-control parameters (Hawkins and Zamba, 2005b). However, the assumption of normality is often not satisfied in many real life situations (Ghute and Shirke, 2012;Zhou et al, 2009). In this study, we investigate the performance of a non-parametric control chart-based Lepage-type test statistic to jointly monitor the shift in mean and/or variance.…”

mentioning

confidence: 99%

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Non-parametric change-point approach for monitoring shifts in process location and variability

Afolabi

Osanaiye

Akpa

2015

IJQET

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Abstract:In statistical process control, detecting if the process is in control and the position of shift in an out-of-control process are critical research problems. If the normality assumption is satisfied, work has advanced in detecting shifts in mean and/or variance. However, the normality assumption is often not satisfied in many real life situations. We suggest a non-parametric Lepage-type change-point (LCP) control chart for jointly detecting process shifts in mean and variance, under non-normality. A comparison between our proposed method and a generalised likelihood ratio (GLR)-based method was made. Process data were simulated following normal and Laplace distributions. The performances of LCP and GLR were assessed and presented, using evaluated average run lengths, under the distributions considered. The LCP competed favourably with the GLR in a normal distribution. However, LCP outperformed GLR under the heavy-tailed distribution considered. We recommend the new approach for short-run situations where the underlying distributions are usually unknown.

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Nonparametric control chart based on change-point model

Abstract: Nonparametric methods, Change-point model, Mann–Whitney statistic, Average run length, Estimated control limits, EWMA chart, 62p30,

Cited by 88 publications

References 32 publications

Selection of the Most Suitable Statistical Process Control Approach for Short Production Runs: A Decision-Model

Selection of the Most Suitable Statistical Process Control Approach for Short Production Runs: A Decision-Model

Distribution-free Phase II Mann–Whitney control charts with runs-rules

Non-parametric change-point approach for monitoring shifts in process location and variability

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