An overview on recent profile monitoring papers (2008–2018) based on conceptual classification scheme

Maleki, Mohammad Reza; Amiri, Amirhossein; Castagliola, Philippe

doi:10.1016/j.cie.2018.10.008

Cited by 88 publications

(32 citation statements)

References 186 publications

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“…In statistical process monitoring (SPM), there have been a number of literature reviews that have shed some light on the current state of a particular topic and in doing so, made way for many publications thereafter; see, for instance, Refs. 1–5. Thus, the tradition emphasized by the latter articles is continued with the generally weighted moving average (GWMA) monitoring schemes in this paper.…”

Section: Introductionmentioning

confidence: 95%

Generally weighted moving average monitoring schemes: Overview and perspectives

Mabude

Malela‐Majika

Castagliola

et al. 2020

Quality & Reliability Eng

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An overview of monitoring schemes from a class called generally weighted moving average (GWMA) is provided. A GWMA scheme is an extended version of the exponentially weighted moving average (EWMA) scheme with an additional adjustment parameter that introduces more flexibility in the GWMA model as it adjusts the kurtosis of the weighting function so that the GWMA scheme can be designed such that it has an advantage over the corresponding EWMA scheme in the detection of certain shift values efficiently. The parametric and distribution‐free GWMA schemes to monitor various quality characteristics and its existing enhanced versions (ie, double GWMA, composite Shewhart‐GWMA, mixed GWMA‐CUSUM, and mixed CUSUM‐GWMA) have better performance than their corresponding EWMA counterparts in many situations; hence, all such existing research works discussing GWMA‐related schemes (ie, 66 publications in total) are documented and categorized in such a manner that it is easy to identify research gaps. Finally, a number of possible future research ideas are provided.

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Section: Introductionmentioning

confidence: 95%

Generally weighted moving average monitoring schemes: Overview and perspectives

Mabude

Malela‐Majika

Castagliola

et al. 2020

Quality & Reliability Eng

Self Cite

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“…They used different monitoring statistics (i.e., transformations of the count data or DR or likelihood ratios) for the EWMA and CUSUM control charts, and showed that the performance of the different statistics is also similar. Maleki et al (2018) provided an overview of the profile monitoring methods since 2008 up to 2018. These papers were also analyzed under different directions for future recommendations.…”

Section: Introductionmentioning

confidence: 99%

Memory type control charts with inverse-Gaussian response: An application to yarn manufacturing industry

Amin

Mahmood

Kinat

2020

Transactions of the Institute of Measurement and Control

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Control charts are commonly applied for monitoring and controlling the performance of the manufacturing process. Usually, control charts are designed based on the main quality characteristics variable. However, there exist numerous other variables which are highly associated with the main variable. Therefore, generalized linear model (GLM)-based control charts were used, which are capable of maintaining the relationship between variables and of monitoring an abrupt change in the process mean. This study is an effort to develop the Phase II GLM-based memory type control charts using the deviance residuals (DR) and Pearson residuals (PR) of inverse Gaussian (IG) regression model. For evaluation, a simulation study is designed, and the performance of the proposed control charts is compared with the counterpart memory less control charts and data-based control charts (excluding the effect of covariate) in terms of the run length properties. Based on the simulation study, it is concluded that the exponential weighted moving average (EWMA) type control charts have better detection ability as compared with Shewhart and cumulative sum (CUSUM) type control charts under the small or/and moderate shift sizes. Moreover, it is shown that utilizing covariate may lead to useful conclusions. Finally, the proposed monitoring methods is implemented on the dataset related to the yarn manufacturing industry to highlight the importance of the proposed control chart.

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“…If a functional relationship can be identified between the quality characteristic

Z

and one or more explanatory variables, then a profile can be defined. Profile monitoring has received a lot of attention in SPM literature: Since the early work of Kang and Albin about simple linear profiles, which are still widely investigated due to their easiness of implementation, see DeMagalhaes and Von Doellinger, Dawod et al, and Mahmood et al, the investigation has been extended to many other functional relationships and nonparametric techniques: Comprehensive reviews can be found in Woodall et al and Maleki and in the book of Noorossana et al Given the growing interest on geometric profile monitoring, see Colosimo et al, which today is becoming more and more precise thanks to the continuous developments in the measurement technologies, in this paper, we focus our attention on SPM of extreme values for geometric profiles. Product quality can be represented by geometric 1D or 2D profiles: In the latter case, we speak about surface monitoring.…”

Section: Introductionmentioning

confidence: 99%

On‐line monitoring of extreme values of geometric profiles in finite horizon processes

Celano

Castagliola

2020

Quality & Reliability Eng

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We investigate the challenges to be faced by quality practitioners to implement a control chart for individuals aimed at monitoring the stability of maximum/minimum realizations of profile quality features. We consider the situation where only a limited amount of inspections can be scheduled, which is typical of the most recent manufacturing applications for the production of small lots of customized products or processes characterized by frequent changeovers. A quantitative study on simulated profiles described by functional data modelled as random Gaussian fields is provided. Finally, we discuss how the proposed control chart can be implemented in a canning process. KEYWORDScontrol chart, generalized extreme value distribution, random Gaussian process, statistical performance Qual Reliab Engng Int. 2020;36:1313-1332.wileyonlinelibrary.com/journal/qre

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An overview on recent profile monitoring papers (2008–2018) based on conceptual classification scheme

Cited by 88 publications

References 186 publications

Generally weighted moving average monitoring schemes: Overview and perspectives

Generally weighted moving average monitoring schemes: Overview and perspectives

Memory type control charts with inverse-Gaussian response: An application to yarn manufacturing industry

On‐line monitoring of extreme values of geometric profiles in finite horizon processes

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