Developing a fuzzy multivariate CUSUM control chart to monitor multinomial linguistic quality characteristics

Ghobadi, Shahram; Noghondarian, Kazem; Noorossana, Rassoul; Mirhosseini, Seyed Mohammad

doi:10.1007/s00170-015-6919-3

Cited by 20 publications

(6 citation statements)

References 30 publications

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“…Erginel and S ¸entürk [26] developed fuzzy EWMA and CUSUM control charts, and they reported that both conventional charts are not able to obtain the uncertainty in the case of incomplete data. Ghobadi et al [27] constructed a fuzzy multivariate cumulative sum (CUSUM) control chart through a numerical comparison via a simulation study on the basis of the average run length (ARL). ey concluded that the fuzzy multivariate cumulative sum (CUSUM) control chart performed better in detecting small-and medium-sized shifts in the process.…”

Section: Literature Reviewmentioning

confidence: 99%

“…erefore, in this study, we will use the fuzzy midrange transformation method for the process of defuzzification of the data. Fuzzy midrange is the midpoint of the ends of the α-level cuts, [27] Manufacturing sector e developed multivariate control chart shows better performance in detecting small-and medium-sized shifts in the process Type-1 fuzzy chart Trapezoidal fuzzy numbers denoted as A α , which is a nonfuzzy set that comprises all elements whose membership is greater than or equal to α-cuts [29]. ere is no theoretical basis supporting any one specifically, and the selection between them should be mainly based on the ease of computation or preference of the user [11].…”

Section: Proposed It2f-scusum Control Chartsmentioning

confidence: 99%

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Interval Type-2 Fuzzy Standardized Cumulative Sum Control Charts in Production of Fertilizers

Razali

Abdullah

Salleh

et al. 2021

Mathematical Problems in Engineering

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Statistical process control is a method used for controlling processes in which causes of variations and correction actions can be observed. Control chart is one of the powerful tools of statistical process control that are used to control nonconforming products. Previous literature suggests that fuzzy charts are more sensitive than conventional control charts, and hence, they provide better quality and conformance of products. Nevertheless, some of the data used are more suitable to be presented in interval type-2 fuzzy numbers compared to type-1 fuzzy numbers as interval type-2 fuzzy numbers have more ability to capture uncertain and vague information. In this paper, we develop an interval type-2 fuzzy standardized cumulative sum (IT2F-SCUSUM) control chart and apply it to data of fertilizer production. This new approach combines the advantages of interval type-2 fuzzy numbers and standardized sample means which can control the variability. Twenty samples with a sample size of six were examined for testing the conformance. The proposed IT2F-SCUSUM control chart unveils that 15 samples are “out of control.” The results are also compared to the conventional CUSUM chart and type-1 fuzzy CUSUM chart. The conventional chart shows that 13 samples are “out of control.” In contrast, the type-1 fuzzy CUSUM chart shows that the process is “out of control” for 14 samples. In the analysis of average run length, the proposed IT2F-SCUSUM chart outperforms the other two CUSUM charts. Thus, we can conclude that the IT2F-SCUSUM chart is more sensitive and takes lesser number of observations to identify the shift in the process. The analyses suggest that the IT2F-SCUSUM chart is a promising tool in examining conformance of the quality of the fertilizer production.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Proposed It2f-scusum Control Chartsmentioning

confidence: 99%

Interval Type-2 Fuzzy Standardized Cumulative Sum Control Charts in Production of Fertilizers

Razali

Abdullah

Salleh

et al. 2021

Mathematical Problems in Engineering

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“…Hwang (2016) propõe uma abordagem melhorada do gráfico MCUSUM baseada em classificação visando detectar rapidamente grandes níveis de deslocamento na média do processo. Ghobadi et al (2015) desenvolveram um gráfico de soma cumulativa multivariada baseada na teoria do conjunto fuzzy para detecção de mudanças pequenas à médias, aplicada à indústria alimentícia. Miekley et al (2013) utilizam a metodologia MCUSUM na detecção precoce de mastite e claudicação utilizando dados de uma fazenda de pesquisa de laticínios.…”

Section: Aplicações Da Mcusumunclassified

Revisão das cartas de controle multivariadas paramétricas

Nunes

Carrir

Rosa³

et al. 2018

RAUnP

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ResumoOs processos industriais atuais, dotados de automação industrial e de um alto volume de dados inerentes aos processos têm exigido o controle das variáveis em tempo real, para que seja possível obter respostas rápidas à detecção e correção de falhas ocorridas durante o processo. A natureza multivariável dos processos industriais exigem métodos mais robustos para se obtenha um controle estatístico efetivo. As Cartas de Controle Multivariadas Paramétricas são amplamente utilizadas no setor industrial para o monitoramento e controle de processos. As Cartas de Controle Multivariadas Paramétricas são cartas de controle tradicionais que pressupõe o conhecimento da distribuição das variáveis, para aplicação dos métodos encontrados na literatura. Neste artigo, se discuti os principais procedimentos de Cartas de Controle Multivariadas Paramétricas encontrados na literatura: Hotelling T², MEWMA e MCUSUM. Assim como suas aplicações nos processos industriais. Considerando as publicações realizadas entre 2006 e 2016, pesquisadas nas principais bases de dados de análise científica. Enfatiza-se neste artigo a necessidade de artigos de revisão, pois estes representam estudos expostos de forma compilada inclinando-se ao desencadeamento de novas ideias e campos de pesquisa. Assim, espera-se que o trabalho apresentado possa servir como fonte de motivação para elaboração de novos estudos e adaptações das implementações aqui discutidas. Palavras-chave:Cartas de controle multivariadas paramétricas; Hotelling T²; MEWMA; MCUSUM. AbstractThe current industrial processes with industrial automation and a high volume of data inherent to the processes have required the control of variables in real time, so you can get quick answers to the detection and correction of failures that have occurred during the process. The multivariate nature of the industrial process requires robust methods to obtain effective statistical control. Parametric Multivariate Control charts are widely used in the industrial sector for the monitoring and process control. Parametric multivariate control charts are traditional control charts that presupposes the knowledge of the distribution of variables, for the application of the methods found in the literature. In this article, we discuss the main procedures of Parametric Multivariate Control charts found in literature: Hotelling T ², MEWMA and MCUSUM. As well as its applications in industrial processes. Considering the publications held between 2006 and 2016, surveyed in the main databases of scientific analysis. This article emphasizes the need for review articles, since they represent compiled expositions, inclined to the triggering of new ideas and fields of research. Thus, it is expected that the presented work can serve as a source of motivation for the elaboration of new studies and adaptations of the implementations discussed here.

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“…It can be concluded that many studies have been conducted on quality state monitor and achieved ideal results. [20][21][22] However, these studies may suffer three weaknesses as follows. First, traditional quality monitoring and analysis methods are mainly carried out just based on the output of the process (data of quality feature) but ignores that the process itself changes (data of state feature) during the manufacturing process.…”

Section: Introductionmentioning

confidence: 99%

State entropy–based fluctuation analysis mechanism for quality state stability in data-driven manufacturing process

Zhao

Yao

2018

Proceedings of the Institution of Mechanical Engineers, Part B:

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Intelligent quality state analysis is a promising tool to deal with manufacturing big data due to its ability in efficiently processing state signals and providing accurate warning results. Inspired by the idea that uses the change of entropy flow to characterize the quality state change, this article proposes a fluctuation analysis mechanism for quality stability based on state entropy in data-driven manufacturing process. First, the multidimensional space cloud model with a three-tuple feature is constructed to describe quality state fluctuation in which the digital features of entropy and hyper-entropy represent the fluctuations’ uncertainty of quality state. Furthermore, in order to quantitatively analyze the fluctuation degree of process state, the entropy change mechanism is introduced into the manufacturing quality state to calculate the state fluctuation degree. The proposed method is validated by a fan blade machining process dataset, and the result shows that the approach could well monitor the quality state fluctuation and show good effect for process stability analysis, which will provide theoretical evidence for the real-time warning and evaluation for abnormal quality state in manufacturing process.

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Developing a fuzzy multivariate CUSUM control chart to monitor multinomial linguistic quality characteristics

Cited by 20 publications

References 30 publications

Interval Type-2 Fuzzy Standardized Cumulative Sum Control Charts in Production of Fertilizers

Interval Type-2 Fuzzy Standardized Cumulative Sum Control Charts in Production of Fertilizers

Revisão das cartas de controle multivariadas paramétricas

State entropy–based fluctuation analysis mechanism for quality state stability in data-driven manufacturing process

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