On the Comparative Efficiency of Change Point Detection in Multivariate Technological Processes Using Multidimensional Double Control Charts
- 作者: Chesalin A.N.1, Grodzensky S.Y.1, Ushkova N.N.1, Bolotin K.V.1, Stavtsev A.V.1
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隶属关系:
- MIREA – Russian Technological University
- 期: 卷 10, 编号 1 (2023)
- 页面: 67-78
- 栏目: SYSTEM ANALYSIS, INFORMATION MANAGEMENT AND PROCESSING, STATISTICS
- URL: https://journal-vniispk.ru/2313-223X/article/view/250554
- DOI: https://doi.org/10.33693/2313-223X-2023-10-1-67-78
- ID: 250554
如何引用文章
详细
The problem of change point detection in multiparametric technological processes having a normal distribution and consisting in a shift from a given value of the sample mean and sample variance is investigated. Various types of control charts are considered, which make it possible to effectively detect simultaneous changes in the mean value and variance in multiparametric technological processes. By the method of statistical modeling, an analysis of the comparative effectiveness of control charts is carried out, practical recommendations are given.
作者简介
Alexander Chesalin
MIREA – Russian Technological University
Email: chesalin_an@mirea.ru
ORCID iD: 0000-0002-1154-6151
Candidate of Engineering; Head of the Department of Computer and Information Security of the MIREA – Russian Technological University
俄罗斯联邦, MoscowSergey Grodzensky
MIREA – Russian Technological University
Email: chesalin_an@mirea.ru
ORCID iD: 0000-0003-1965-5624
Doctor of Engineering, Professor; Professor at the Department of Computer and Information Security of the MIREA – Russian Technological University
俄罗斯联邦, MoscowNadezhda Ushkova
MIREA – Russian Technological University
Email: chesalin_an@mirea.ru
assistant at the Department of Computer and Information Security of the MIREA – Russian Technological University
俄罗斯联邦, MoscowKirill Bolotin
MIREA – Russian Technological University
Email: chesalin_an@mirea.ru
assistant at the Department of Computer and Information Security of the MIREA – Russian Technological University
俄罗斯联邦, MoscowAlexey Stavtsev
MIREA – Russian Technological University
编辑信件的主要联系方式.
Email: chesalin_an@mirea.ru
Candidate of Physics and Mathematics; associate professor at the Department of Computer and Information Security of the MIREA – Russian Technological University
俄罗斯联邦, Moscow参考
- Montgomery D. Introduction to statistical quality control. 7th ed. Wiley, 2013. 754 p.
- Wheeler D.J., Chambers D.S. Understanding statistical process control. 3th ed. SPC Press. 2010.
- Jalilibal Z., Amiri A., Castagliola P., Khoo M. Monitoring the coefficient of variation: A literature review. Computers & Industrial Engineering. 2021. No. 161. doi: 10.1016/j.cie.2021.107600.
- Sabahno H., Celano G. Monitoring the multivariate coe-cient of variation in presence of autocorrelation with variable parameters control charts. Quality Technology and Quanti-tative Management. 2022. doi: 10.1080/16843703.2022.2075193.
- Alekseeva A.V., Kliachkin V.N. Selection of the parameters of the generalized dispersion algorithm for multivariate statistical control of the process scattering. Proceedings of the Samara Scientific Center of the Russian Academy of Sciences. 2021. Vol. 23. No.1. Pp. 79–83. (In Rus.)
- Adler IU.P., Shper V.L. Deming’s science and its destiny: textbook. Moscow: NITU “MISiS”, 2021. 352 p.
- Kliachkin V.N. Multidimensional statistical control of technological process. Moscow: Finance and Statistics, 2022. 192 p.
- Fam Van Tu, Chesalin A.N., Grodzenskii IA.S., Emanakov I.V. Improving the effectiveness of the control card using fuzzy sets. Quality and Life. 2021. No. 2 (30). Pp. 37–43. (In Rus.)
- Chesalin A.N., Grodzenskii S. Ia., Nilov M.U., Fam Van Tu. Intelligent quality management tools of digital production. Standards and Quality. 2020. No. 3. Pp. 68–72. (In Rus.)
- Bersimis S., Sgora A., Psarakis S. A robust meta‐method for interpreting the out‐of‐control signal of multivariate control charts using artificial neural networks. Quality and Reliability Engineering International. 2021. Pp. 1–34. doi: 10.1002/qre.2955.
- Shiriaev A.N. Probabilistic-statistical methods in decision theory. 2nd ed. Moscow: MTSNMO, 2014. 144 p.
- Chesalin A.N. Research of the effectiveness of change detection in technological processes based on statistical modeling. Nonlinear World. 2022. Vol. 20. No. 3. Pp. 28−34. (In Rus.) doi: 10.18127/j20700970-202203-03.
- Chen G., Cheng S.W. Multivariate Max-chart. Economic Quality Control. 2006. Vol. 21. No. 1. Pp. 113–125.
- Kruba R, Mashuri M, Prastyo D. The effectiveness of Max-half-Mchart over Max-Mchart in simultaneously monitoring process mean and variability of individual observations. Quality and Reliability Engineering International. 2021. Pp. 1–14. doi: 10.1002/qre.2860.
- Cheng S.W., Mao H. A Multivariate semi-circle control chart for variables data. Quality Technology & Quantitative Management. 2008. Vol. 5. No. 4. Pp. 331–338, doi: 10.1080/16843703.2008.11673405.
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