Using Statistical Methods to Assess the Accuracy of Production Process Control Systems

Abstract:

In today's highly competitive industrial landscape, maintaining the accuracy of production processes is paramount. This article explores the critical role of statistical methods in evaluating and enhancing the precision of production process control systems. Statistical techniques offer a data-driven approach to monitor and optimize production processes, enabling early issue detection, resource optimization, and continuous improvement. By assessing the accuracy of these control systems, organizations can not only deliver higher-quality products but also gain a competitive edge, reduce risks, and enhance customer satisfaction. While production process control systems are critical for maintaining product quality and efficiency, several challenges persist in their implementation. Variability in raw materials, equipment wears and tear, and external factors can all contribute to deviations from desired standards. These challenges necessitate a robust approach to control and monitoring, which is where statistical methods play a pivotal role. One of the fundamental statistical tools used in assessing the accuracy of production process control systems is hypothesis testing. By formulating hypotheses regarding process parameters and conducting statistical tests, manufacturers can determine whether their processes are operating within acceptable limits. This article underscores the significance of integrating statistical methods into modern manufacturing practices and highlights the benefits they bring to industries seeking sustainable growth.

• Periodical: Engineering Headway (Volume 7)
• Pages: 113-123
• DOI: https://doi.org/10.4028/p-UdgL54
• Online since: April 2024
• Authors: Farrukh Mamedov*, Viktor Artemyev, Vitaly Kargin
• Keywords: Error Coefficient Method, Quantization Period, Statistical Methods, Transfer Function

References

[1] Rutkowski L. A Comparison of the Frequency Modulation Transfer Function with the Modulation Transfer Function in a Room / L. Rutkowski // Applied Acoustics. – 1996. – Vol. 49, No. 4. – P. 307-320. – EDN AKOOSX.

DOI: 10.1016/s0003-682x(96)00028-x

[2] V. V. Vasilev, A. V. Vishnyakov, G. Yu. Sidorov, V. A. Stuchinsky. Method for Measuring the Modulation Transfer Function of IR Objective. Optoelectronics, Instrumentation and Data Processing. – 2022. – Vol. 58, No. 3. – P. 250-256. – DOI 10.3103/S8756699022030086. – EDN OBUCHN.

DOI: 10.3103/s8756699022030086

[3] Artemyev V. S. Automation of the existing methodology of management and integration tools / V. S. Artemyev, S. D. Savostin // Social security in the Eurasian space: Materials of the II International Scientific Conference, Moscow-Tyumen, December 16, 2022 / Edited by I.A. Grosheva. – Moscow-Tyumen: Autonomous non–profit organization of higher Education "Institute of Business Career", 2023. - pp.168-172.

[4] Artemyev V. S. Automation of control methods of cooperation in mathematical modeling systems / V. S. Artemyev, E. A. Nazoikin, S. D. Savostin // Development of agricultural industries based on the formation of an effective management mechanism: collection of scientific papers of the IV International Scientific and Practical Conference, Kirov, November 16, 2022. – Kirov: Federal State Budgetary Educational Institution of Higher Education Vyatka State Agrotechnological University, 2022. – pp.307-309

DOI: 10.17238/issn2587-666x.2023.3.55

[5] Gamayunov N. I. A method of statistical modeling to estimate the error in determining the coefficient of moisture diffusion / N. I. Gamayunov, R. A. Ispiryan, A. A. Sheinman // Journal of Engineering Physics. – 1980. – Vol. 38, No. 4. – P. 429-434. – DOI 10.1007/BF00866478. – EDN ZYWXPZ.

DOI: 10.1007/bf00866478

[6] Ritchie-Scott, A. Note on the Probable Error of the Coefficient of Correlation in the Variate Difference Correlation Method / A. Ritchie-Scott // Biometrika. – 1915. – Vol. 11, No. 1-2. – P. 136. – EDN ILMTBD.

DOI: 10.2307/2331885

[7] Rybakov K. A. Sufficient Epsilon-Optimality Conditions for Systems with Random Quantization Period / K. A. Rybakov // Journal of Mathematical Sciences. – 2021. – DOI 10.1007/s10958-021-05413-3. – EDN OZRVBA.

DOI: 10.1007/s10958-021-05413-3

[8] Dobrolyubov, I. P. Choice of quantization period in expert metering system for combustion engines / I. P. Dobrolyubov, O. F. Savchenko // Information technologies, systems and devices in the agro-industrial complex: Materials of the International Scientific and Practical Conference, Novosibirsk, October 17-18, 2006 – P. 251-252. – EDN VEFXQF.

[9] S. A. Mokrushin, E. A. Nazoikin, S. D. Savostin, V. S. Artemyev. Exploitation of automated systems and technologies in the existing aspects of digital transformation for agricultural holdings. Development of agricultural industries based on the formation of an effective management mechanism: collection of scientific papers of the IV International Scientific and Practical Conference, Kirov, November 16, 2022. – Kirov: Federal State Budgetary Educational Institution of Higher Education Vyatka State Agrotechnological University, 2022. – pp.143-146

DOI: 10.17238/issn2587-666x.2023.3.55