On the Comparative Efficiency of Change Point Detection in Multivariate Technological Processes Using Multidimensional Double Control Charts
( Pp. 67-78)
Chesalin Alexander N.
Grodzensky Sergey Ya.
N. Ushkova Nadezhda
Bolotin Kirill V.
Stavtsev Alexey V.
More about authors
Chesalin Alexander N.
Candidate of Engineering; Head of the Department of Computer and Information Security
MIREA – Russian Technological University
Moscow, Russian Federation Grodzensky Sergey Ya. Doctor of Engineering, Professor; Professor at the Department of Computer and Information Security N. Ushkova Nadezhda assistant at the Department of Computer and Information Security Bolotin Kirill V. assistant at the Department of Computer and Information Security Stavtsev Alexey V. Candidate of Physics and Mathematics; associate professor at the Department of Computer and Information Security
MIREA – Russian Technological University
Moscow, Russian Federation Grodzensky Sergey Ya. Doctor of Engineering, Professor; Professor at the Department of Computer and Information Security N. Ushkova Nadezhda assistant at the Department of Computer and Information Security Bolotin Kirill V. assistant at the Department of Computer and Information Security Stavtsev Alexey V. Candidate of Physics and Mathematics; associate professor at the Department of Computer and Information Security
Abstract:
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.
How to Cite:
Chesalin A.N., Grodzensky S.Ya., Ushkova N.N., Bolotin K.V., Stavtsev A.V. On the Comparative Efficiency of Change Point Detection in Multivariate Technological Processes Using Multidimensional Double Control Charts. Computational Nanotechnology. 2023. Vol. 10. No. 1. Pp. 67–78. (In Rus.) DOI: 10.33693/2313-223X-2023-10-1-67-78
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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.
Keywords:
multiparametric technological process, change point detection, multidimensional double control charts, statistical control of technological processes, Monte Carlo method, heat maps.
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