Monitoring multivariate–attribute processes based on transformation techniques

Doroudyan, Mohammad Hadi; Amiri, Amirhossein

doi:10.1007/s00170-013-5149-9

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…Recently, the same authors proposed and analysed a one-sided version of that scheme (Laungrungrong, Borror, and Montgomery 2014). Dourodyan and Amiri (2013) proposed data transformations for monitoring multivariate attribute processes, that is, processes whose quality is represented by correlated variables and attribute quality characteristics. Aparisi, García-Bustos, and Epprecht (2014) developed and made available friendly software to optimise and compare the performances of the multiple scheme (one chart per variable) and of three multivariate schemes (namely the MP, DF and MX charts) in order to support the final user in the decision of the scheme to employ in each particular case.…”

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confidence: 98%

Optimal EWMA of linear combination of Poisson variables for multivariate statistical process control

García-Bustos

Aparisi

Epprecht

2014

International Journal of Production Research

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In this paper, we propose a new process control chart for monitoring correlated Poisson variables, the EWMA LCP chart. This chart is the exponentially weighted moving average (EWMA) version of the recently proposed LCP chart. The latter is a Shewhart-type control chart whose control statistic is a linear combination of the values of the different Poisson variables (elements of the Poisson vector) at each sampling time. As a Shewhart chart, it is effective at signalling large process shifts but is slow to signal smaller shifts. EWMA charts are known to be more sensitive to small and moderate shifts than their Shewhart-type counterparts, so the motivation of the present development is to enhance the performance of the LCP chart by the incorporation of the EWMA procedure to it. To ease the design of the EWMA LCP chart for the end user, we developed a user-friendly programme that runs on Windows© and finds the optimal design of the chart, that is, the coefficients of the linear combination as well as the EWMA smoothing constant and chart control limits that together minimise the out-of-control ARL under a constraint on the in-control ARL. The optimization is carried out by genetic algorithms where the ARLs are calculated through a Markov chain model. We used this programme to evaluate the performance of the new chart. As expected, the incorporation of the EWMA scheme greatly improves the performance of the LCP chart.

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confidence: 98%

Optimal EWMA of linear combination of Poisson variables for multivariate statistical process control

García-Bustos

Aparisi

Epprecht

2014

International Journal of Production Research

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“…As mentioned before, in the multispecification process case, independent assumption is violated, and neglecting the correlation leads to misleading results. Doroudyan and Amiri 22,23 proposed transformation methods to monitor a combined vector.…”

Section: Introductionmentioning

confidence: 99%

Monitoring combined multivariate process approaching hybrid kernel‐CUSUM analysis

Atashgar

Saeedian

NamAvar

2021

Quality & Reliability Eng

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In some complicated cases, a multivariate process/product can be characterized better by a combined technical specification vector. A combined technical specification vector is referred to as the case that the quality of a process/product is evaluated by different continuous and discrete random variables at the same time. In this case that there is also the covariance condition, the vector could not follow a single joint distribution. This research approaching kernel method and support vector data description (SVDD) proposes a new multivariate cumulative scheme named MK‐CUSUM to monitor the random vector of a process/product with combined technical specifications. The numerical analysis indicates that the proposed scheme is capable of detecting out‐of‐control conditions effectively when the process experiences different shifts in the random mean vector. The comparative performance report addresses that the proposed scheme is superior compared to the models of literature.

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“…The first attribute quality characteristic may be checked with a gauge whereas the latter checked visually (Rezazadeh, 2014). To the best of our knowledge, there are few works provided in the literature in order to monitor multivariate-attribute processes, such as Doroudyan and Amiri (2013). Doroudyan and Amiri (2014) also designed a control scheme for monitoring multivariate-attribute processes.…”

Section: Introductionmentioning

confidence: 99%

Estimating the time of a step change in the multivariate-attribute process mean using ANN and MLE

Amiri

Maleki

Sogandi

2018

IJDATS

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In this paper, we consider correlated multivariate-attribute quality characteristics and provide two methods including a modular method based on artificial neural network (ANN) as well as maximum likelihood estimation (MLE) method to estimate the time of change in the parameters of the process mean. We evaluate the performance of the estimators in terms of some criteria in change point estimation and compare them through simulation studies. The results show that the proposed ANN-based model outperforms the MLE approach under most step shifts in the mean vector of the multivariate-attribute process.

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Monitoring multivariate–attribute processes based on transformation techniques

Cited by 5 publications

References 26 publications

Optimal EWMA of linear combination of Poisson variables for multivariate statistical process control

Optimal EWMA of linear combination of Poisson variables for multivariate statistical process control

Monitoring combined multivariate process approaching hybrid kernel‐CUSUM analysis

Estimating the time of a step change in the multivariate-attribute process mean using ANN and MLE

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