Selection of a Ship Compressor Using Statistical Data Processing

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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.

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About the authors

Vadim A. Tsvetkov

ITMO University

Author for correspondence.
ORCID iD: 0000-0003-4357-0022

Posgraduate Student, Professor Assistant

Russian Federation, Saint Petersburg

Vladimir A. Pronin

ITMO University

ORCID iD: 0000-0002-9278-5903
SPIN-code: 3737-3495

Dr. Sci. (Tech.), Professor

Russian Federation, Saint Petersburg

Alexander V. Kovanov

ITMO University

ORCID iD: 0000-0003-2821-795X

Posgraduate Student

Russian Federation, Saint Petersburg

Ekaterina N. Mikhailova

ITMO University

ORCID iD: 0000-0002-2700-0348

Posgraduate Student

Russian Federation, Saint Petersburg


  1. Goflin AP, Shilov VD. Ship compressor machines: a textbook for shipbuilding specialties of universities. Leningrad: Sudostroyeniye; 1977. (In Russ).
  2. Tigarev PA. Handbook of ship compressors. Leningrad: Sudostroyeniye; 1981.
  3. Mkhitaryan VS. Data analysis: a textbook for academic undergraduate studies. Mkhitaryan V.S. editor. Moscow: Yurayt; 2019. (In Russ).
  4. 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).
  5. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. Berlin: Springer; Springer, 2014.
  6. Wasserman L. All of Statistics A Concise Course in Statistical Inference. New York: Springer-Verlag; 2004. 422 p.
  7. Michie D., Spiegelhalter D.J., Taylor C.C. Machine Learning, Neural and Statistical Classification. Technometrics, 37(4), 1994. Available from:
  8. James G, Witten D, Hastie T, et al. Applied Logistic Regression. 3rd ed. New York: John Wiley & Sons; 2013.
  9. Babeshko LO. Econometrics and econometric modeling: textbook. Moscow: Vuzovskiy uchebnik: INFRA-M; 2019. (In Russ).
  10. Eliseeva II. Econometrics: a textbook for undergraduate and graduate studies. Moscow: Yurayt; 2017. (In Russ).
  11. De Prado Marcos Lopez. Machine Learning: Algorithms for Business. St. Petersburg: Peter; 2019. (In Russ).
  12. James G, Witten D, Hastie T, et al. An Introduction to Statistical Learning: With Applications in R. Berlin: Springer; 2013.
  13. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Berlin: Springer; 2009.
  14. Bishop CM. Pattern Recognition And Machine Learning. New Delhi: Springer India; 2013.
  15. Statement and solution of the problem using the add-in “Search for a solution” Excel for Microsoft [internet]. Available from:постановка-и-решение-задачи-с-помощью-надстройки-поиск-решения-5d1a388f-079d-43ac-a7eb-f63e45925040 Accessed: 11.08.2022.

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