Selection of a Ship Compressor Using Statistical Data Processing

Abstract

INTRODUCTION: Compressors are essential for supplying compressed air at various pressures and flow rates to the ship’s power systems, and to the ship as a whole. An appropriate choice of the compressor is essential for the proper operation of water transport infrastructure. Hence, it is necessary to consider this issue in detail. If all variables, the compressor selection parameters to be considered, were measured in the same scales and units, it would be possible to suggest adding up all values, but this approach is very crude. The solution is to normalize the values of the variables and then calculate a final criterion based on them.

AIM: Finding a formal criterion by which the selection of a particular compressor will be made.

MATERIAL AND METHODS: The process of selecting a compressor for starting the ship’s engines and for the general operation of the ship is discussed based on exponential and linear rationing methods. Certain parameters of compressors from domestic and foreign manufacturers are offered for statistical processing. Subsequently, data processing was carried out using maximum likelihood estimation and logistic regression.

RESULTS: The result of the study is a single formal criterion, the final rating, instead of several qualitative parameters. A set of characteristics has been determined that describes the optimal compressor based on the above calculations and data processing. A simulation of the probability of assigning a compressor, with a certain set of characteristics, to a positive or negative class has been performed. The training of the model was done using the available training data.

CONCLUSION: Statistical methods of data processing can be applied to an object such as a compressor. Given a set of desired compressor characteristics, it is possible to teach the machine to determine the optimal compressor.

Keywords

compressor equipment / ship compressor / compressor selection / statistics / normalization / maximum likelihood method / logistic regression / probability / machine learning

References

[1] Goflin AP, Shilov VD. Ship compressor machines: a textbook for shipbuilding specialties of universities. Leningrad: Sudostroyeniye; 1977. (In Russ).

[2] Гофлин А.П., Шилов В.Д. Судовые компрессорные машины: учебник для судостроит. специальностей вузов. Л.: Судостроение, 1977.

[3] Tigarev PA. Handbook of ship compressors. Leningrad: Sudostroyeniye; 1981.

[4] Тигарев П.А. Справочник по судовым компрессорам. Л.: Судостроение, 1981.

[5] Mkhitaryan VS. Data analysis: a textbook for academic undergraduate studies. Mkhitaryan V.S. editor. Moscow: Yurayt; 2019. (In Russ).

[6] Мхитарян В.С. Анализ данных: учебник для академического бакалавриата / под редакцией В. С. Мхитаряна. Москва: Юрайт, 2019.

[7] Kovalev EA. Probability Theory and Mathematical Statistics for Economists: Textbook and Workshop for High Schools. Medvedev GA., editor. 2nd ed., Moscow: Yurayt; 2020. (In Russ).

[8] Ковалев Е.А. Теория вероятностей и математическая статистика для экономистов: учебник и практикум для вузов / под общей редакцией Медведева Г. А. 2-е изд., испр. и доп. Москва: Издательство Юрайт, 2020.

[9] Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. Berlin: Springer; Springer, 2014.

[10] Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning. Berlin: Springer, 2014.

[11] Wasserman L. All of Statistics A Concise Course in Statistical Inference. New York: Springer-Verlag; 2004. 422 p.

[12] Wasserman L. All of Statistics A Concise Course in Statistical Inference. New York: Springer-Verlag, 2004.

[13] Michie D., Spiegelhalter D.J., Taylor C.C. Machine Learning, Neural and Statistical Classification. Technometrics, 37(4), 1994. Available from: https://www1.maths.leeds.ac.uk/~charles/statlog/whole.pdf

[14] Michie D., Spiegelhalter D.J., Taylor C.C. Machine Learning, Neural and Statistical Classification. Technometrics, 37(4), 1994. Режим доступа: https://www1.maths.leeds.ac.uk/~charles/statlog/whole.pdf

[15] James G, Witten D, Hastie T, et al. Applied Logistic Regression. 3rd ed. New York: John Wiley & Sons; 2013.

[16] Hosmer Jr. D.W., Lemeshow S., Sturdivant R.X. Applied Logistic Regression. 3rd ed. New York: John Wiley & Sons, 2013.

[17] Babeshko LO. Econometrics and econometric modeling: textbook. Moscow: Vuzovskiy uchebnik: INFRA-M; 2019. (In Russ).

[18] Бабешко Л.О. Эконометрика и эконометрическое моделирование: учебник. Москва: Вузовский учебник: ИНФРА-М, 2019.

[19] Eliseeva II. Econometrics: a textbook for undergraduate and graduate studies. Moscow: Yurayt; 2017. (In Russ).

[20] Елисеева И.И. Эконометрика: учебник для бакалавриата и магистратуры. Москва: Юрайт, 2017.

[21] De Prado Marcos Lopez. Machine Learning: Algorithms for Business. St. Petersburg: Peter; 2019. (In Russ).

[22] Де Прадо Маркос Лопес: Машинное обучение: алгоритмы для бизнеса. СПб.: Питер, 2019.

[23] James G, Witten D, Hastie T, et al. An Introduction to Statistical Learning: With Applications in R. Berlin: Springer; 2013.

[24] James G., Witten D., Hastie T., et al. An Introduction to Statistical Learning: With Applications in R. Berlin: Springer, 2013.

[25] The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Berlin: Springer; 2009.

[26] The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Berlin: Springer, 2009.

[27] Bishop CM. Pattern Recognition And Machine Learning. New Delhi: Springer India; 2013.

[28] Bishop C.M. Pattern Recognition And Machine Learning. New Delhi: Springer India, 2013.

[29] Statement and solution of the problem using the add-in “Search for a solution” Excel for Microsoft [internet]. Available from: https://support.microsoft.com/ru-ru/office/постановка-и-решение-задачи-с-помощью-надстройки-поиск-решения-5d1a388f-079d-43ac-a7eb-f63e45925040 Accessed: 11.08.2022.

[30] Постановка и решение задачи с помощью надстройки «Поиск решения» Excel для Microsoft [internet]. – Режим доступа: https://support.microsoft.com/ru-ru/office/постановка-и-решение-задачи-с-помощью-надстройки-поиск-решения-5d1a388f-079d-43ac-a7eb-f63e45925040 Дата обращения: 11.08.2022.

RIGHTS & PERMISSIONS

Eco-Vector