How to find the variables causing outliers in a multivariate autocorrelated process control: a study in simulation and an extruder machine-Reference-Cited by-同舟云学术

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How to find the variables causing outliers in a multivariate autocorrelated process control: a study in simulation and an extruder machine

Published:2022-08-23 Issue:3-4 Volume:122 Page:1497-1511
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Ueda Renan Mitsuo^ORCID,Souza Adriano Mendonça^ORCID

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00170-022-10000-0.pdf

Reference37 articles.

1. Li S, Chen Y, Tong C (2021) Multivariate statistical process monitoring based on principal discriminative component analysis. J Franklin Inst 358(15):7900–7915. https://doi.org/10.1016/j.jfranklin.2021.07.041

2. Golilarz NA, Addeh A, Gao H, Ali L, Roshandeh AM, Munir HM, Khan RU (2019) A new automatic method for control chart patterns recognition based on ConvNet and harris hawks meta heuristic optimization algorithm. Ieee Access 7:149398–149405. https://doi.org/10.1109/ACCESS.2019.2945596

3. Montgomery DC, Runger GC (2018) Applied statistics and probability for engineers. Wiley, Hoboken, NJ

4. Nidsunkid S, Lao M, Thaithanan J (2022) The impact of a random vector with variables from normal and non-normal distributions on multivariate control charts. Qual Reliab Eng Int 38(1):447–471. https://doi.org/10.1002/qre.2989

5. Aytaçoğlu B, Driscoll AR, Woodall WH (2021) Controlling the conditional false alarm rate for the MEWMA control chart. J Qual Technol 1–16. https://doi.org/10.1080/00224065.2021.1947162

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