Step-Down diagnostic analysis for monitoring the covariance matrix of bivariate normal processes

Mingoti, Sueli Aparecida; Pinto, Letícia Pereira

doi:10.1080/03610918.2018.1458137

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…The method proposed by Sullivan et al [2007] is also applicable for non-normal multivariate data. Mingoti and Pinto [2018] showed that the method of Sullivan et al (2007) has better detection power than both VMAX and VMIX control charts under the bivariate normal assumption. Zou et al [2011] considered the sparsity feature in high-dimensional processes and argued that a fault diagnosis problem is similar to the variable or model selection problem in this setting.…”

Section: Interpretation Of Out-of-control Signalsmentioning

confidence: 99%

Statistical Monitoring of the Covariance Matrix in Multivariate Processes: A Literature Review

Ebadi¹,

Chenouri²,

Lin³

et al. 2020

Preprint

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Monitoring several correlated quality characteristics of a process is common in modern manufacturing and service processes. For this purpose, control charts have been developed to detect assignable causes before producing nonconforming products. Although a lot of attention has been paid to monitoring the multivariate process mean, not many control charts are available for monitoring the covariance matrix. This paper presents a comprehensive overview of the literature on control charts for monitoring covariance matrix in multivariate statistical process monitoring (MSPM) framework. It classifies the research that have previously appeared in the literature. We highlight the challenging areas for research and provide some directions for future research.

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