Developing a univariate approach to phase-I monitoring of fuzzy quality profiles

Noghondarian, Kazem; Ghobadi, Shahram

doi:10.5267/j.ijiec.2012.05.008

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…Ho et al (2010) proposed to monitor profiles by the slope, intercept and variance of the linear model and identify out-of-control samples by F statistics. Noghondarian and Ghobadi (2012) developed a model in fuzzy linear regression to construct the quality profiles. Kalaei et al (2017) introduced the U statistic for a multistage process involving the cascade property in phase I monitoring of simple linear profiles.…”

Section: Introductionmentioning

confidence: 99%

Phase I non-linear profiles monitoring using a modified Hausdorff distance algorithm and clustering analysis

Nie

Liu

et al. 2020

IJQRM

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PurposeThe purpose of this paper is to propose a new non-parametric phase I control chart for the problem of non-linear profile outlier detection.Design/methodology/approachThe proposed non-parametric method is based on a modified Hausdorff distance, which does not require a restrictive assumption on the form of profiles. By obtaining the distance between each profile and the baseline profile, the authors introduced an iterative optimization clustering algorithm to identify outliers by clustering distances.FindingsThe simulation results show that the proposed method can distinguish outliers for structural changes of non-linear profiles. The authors also present a real industrial case example to highlight how practitioners can implement and make use of the proposed control chart in outlier detection applications, and it achieves higher accuracy in the outlier detection of complex profiles.Practical implicationsThe research results of this paper can be applied to any manufacturing or service system whose quality characteristics are characterized by non-linear profiles. This new approach provides quality practitioners a better decision-making tool for non-linear profile outlier detection.Originality/valueDue to the complexity of real-world applications, the non-linear profiles monitoring problem is yet to be addressed. However, the related research still remains rare. And the authors’ proposed non-linear profile control chart, which does not require a restrictive assumption on the form of profiles, shows its applicability and superiority in simulation study and real-world case.

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Section: Introductionmentioning

confidence: 99%

Phase I non-linear profiles monitoring using a modified Hausdorff distance algorithm and clustering analysis

Nie

Liu

et al. 2020

IJQRM

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“…Most studies that have been published in the literature focus either on phase I or on phase II. Kim et al, Zhang et al, Saghaei et al, Mahmoud et al, Noorossana and Zerehsaz, De Magalhães and Von Doellinger, Kamranrad and Amiri, Kazemzadeh et al, Khedmati and Niaki, Chiang et al, and Zhang et al investigated linear profile monitoring in phase II, whereas Mahmoud and Woodall, Mahmoud et al, Noghondarian and Ghobadi, Amiri and Moein, Yeh and Zerehsaz, Ghobadi et al, Riaz and Touqeer, and Taghipour et al focused on phase I linear profile monitoring. Methods for monitoring nonlinear profiles have been proposed by Vaghefi et al, Ding et al, Jin and Shi, Jensen and Birch, Walker and Wright, and Steiner et al For a more thorough review about profile monitoring approaches in phases I and II, we refer to the review paper by Woodall and the textbook edited by Noorossana et al An overview on recent profile monitoring papers (2008‐2018) based on a conceptual classification scheme has been done by Maleki et al…”

Section: Introductionmentioning

confidence: 99%

A new Bayesian multivariate exponentially weighted moving average control chart for phase II monitoring of multivariate multiple linear profiles

Yazdi

Hamadani

Amiri

et al. 2019

Quality & Reliability Eng

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Recently, statistical profile monitoring methods have become efficient tools for monitoring the quality of a product (or a production process) using control charts. The key idea is to describe the relationship between a response variable and a set of explanatory variables in the form of a statistical regression model, which called profile. Traditionally, those control charts are constructed with standard “frequentistic” regression models. Recently, it has been proposed to apply Bayesian regression models instead, and it has been empirically demonstrated that Bayesian regression models have the potential to perform significantly better. In this paper, we introduce a novel Bayesian multivariate exponentially weighted moving average control chart for monitoring multivariate multiple linear profiles in phase II. The key idea is to use the data from historical data sets to generate informative prior distributions for the regression models in phase II. The results of our empirical simulation studies show that the Bayesian multivariate multiple linear regression model is superior to its classical “frequentistic” counterpart in terms of the average run length. Our empirical findings are in agreement with findings reported in recently published articles. To shed more light onto the merit of the proposed Bayesian method, we carry out a sensitivity analysis, in which we investigate how the amount of phase I data influences the results. We also demonstrate the applicability and superiority of the proposed Bayesian method by a real‐world application.

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“…Few studies have already been published in the field of fuzzy linear profiles, two studies in phase I and one study in phase II. Noghondarian and Ghobadi (2012) proposed a univariate approach for monitoring phase I fuzzy quality profiles. Ghobadi et al (2012) proposed a multivariate approach for monitoring process/product fuzzy quality profiles in phase I.…”

Section: Introductionmentioning

confidence: 99%

New fuzzy EWMA control charts for monitoring phase II fuzzy profiles

Moghadam

Ali²,

Ardali³

et al. 2016

10.5267/j.dsl

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In many quality control applications, the quality of a process or product is explained by the relationship between response variable and one or more explanatory variables, called a profile. In this paper, a new fuzzy EWMA control chart for phase II fuzzy profile monitoring is proposed. To this end, we extend EWMA control charts to its equivalent Fuzzy type and then implement fuzzy ranking methods to determine whether the process fuzzy profile is under or out of control. The proposed method is capable of identifying small changes in process under condition of process profile explaining parameters vagueness, roughness and uncertainty. Determining the source of changes, this method provides us with the possibility of recognizing the causes of process transition from stable mode, removing these causes and restoring the process stable mode.

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Developing a univariate approach to phase-I monitoring of fuzzy quality profiles

Cited by 9 publications

References 22 publications

Phase I non-linear profiles monitoring using a modified Hausdorff distance algorithm and clustering analysis

Phase I non-linear profiles monitoring using a modified Hausdorff distance algorithm and clustering analysis

A new Bayesian multivariate exponentially weighted moving average control chart for phase II monitoring of multivariate multiple linear profiles

New fuzzy EWMA control charts for monitoring phase II fuzzy profiles

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