METHOD OF FORECASTING THE RESOURCE OF RAILWAY ROLLING STOCK COMPONENTS AND PARTS USING MACHINE LEARNING
Rubrics: TRANSPORT SYSTEMS
Authors:
1.
Russian University of Transport
(Department of Non-traction Rolling Stock, Professor)
employee
Moscow, Moscow, Russian Federation
employee
Moscow, Moscow, Russian Federation
2.
Russian University of Transport
(Department of Non-traction Rolling Stock, Associate Professor)
employee
Moscow, Moscow, Russian Federation
employee
Moscow, Moscow, Russian Federation
3.
Russian University of Transport
(Department of Non-traction Rolling Stock, Associate Professor)
employee
Moscow, Moscow, Russian Federation
employee
Moscow, Moscow, Russian Federation
4.
Bauman Moscow State Technical University
(Student)
student
Moscow, Moscow, Russian Federation
student
Moscow, Moscow, Russian Federation
Type:
Article
Pages:
from 42
to 49
Status:
Published
Received:
11.09.2023
Accepted:
24.10.2023
Published:
30.11.2023
Subject area:
VAC 2.9.3 Подвижной состав железных дорог, тяга поездов и электрификация
UDK 004.45 Cистемное программное обеспечение
BBK 392 Железнодорожный транспорт
UDK 004.45 Cистемное программное обеспечение
BBK 392 Железнодорожный транспорт
Language:
Russian
Keywords:
developments, processes, production, optimization, enterprise, wheel pair, artificial intelligence, learning
Abstract (English):
The study objective is to develop a system for forecasting the service life of components and parts of railway rolling stock. The task to which the paper is devoted is to determine the service life of wheel pairs of railway rolling stock before their next turning. Research methods include a technique for forecasting the resource of components and parts of railway wheel pairs using a system for estimating expected parameters, including wheel run. The study includes the use of three different machine learning algorithms: linear regression, random forest and gradient boosting. The trained models of each algorithm are shown, as well as the convergence of MSE, MAPE an d R-squared metrics at each iteration of learning. The novelty of the work: the study results make it possible to predict the period of wheel pairs operation with high accuracy and include a feedback mechanism for automating and updating the model, which increases the accuracy of forecasting. Research results: on the basis of the proposed method, a system is developed that allows determining the time interval of operation of rolling stock wheel pairs before their next turning. Conclusions: the proposed method will make it possible to predict the period of operation of certain components and parts under the conditions of a given proving ground until their working condition is restored, based on a system for evaluating expected parameters using machine learning.
The study objective is to develop a system for forecasting the service life of components and parts of railway rolling stock. The task to which the paper is devoted is to determine the service life of wheel pairs of railway rolling stock before their next turning. Research methods include a technique for forecasting the resource of components and parts of railway wheel pairs using a system for estimating expected parameters, including wheel run. The study includes the use of three different machine learning algorithms: linear regression, random forest and gradient boosting. The trained models of each algorithm are shown, as well as the convergence of MSE, MAPE an d R-squared metrics at each iteration of learning. The novelty of the work: the study results make it possible to predict the period of wheel pairs operation with high accuracy and include a feedback mechanism for automating and updating the model, which increases the accuracy of forecasting. Research results: on the basis of the proposed method, a system is developed that allows determining the time interval of operation of rolling stock wheel pairs before their next turning. Conclusions: the proposed method will make it possible to predict the period of operation of certain components and parts under the conditions of a given proving ground until their working condition is restored, based on a system for evaluating expected parameters using machine learning.
Keywords:
developments, processes, production, optimization, enterprise, wheel pair, artificial intelligence, learning
developments, processes, production, optimization, enterprise, wheel pair, artificial intelligence, learning
1. Plas JV. Python data science handbook. Moscow: Piter; 2018.
2. Beysolow T.II. Applied reinforcement learning with Python: with OpenAI gym, tensorflow, and keras. Apress; 2019.
3. Kolesnikova GI. Artificial intelligence: problems and prospects. Videohauka [Internet]. 2018;2(10). Available from: https://videonauka.ru/stati/44-novye-tekhnologii/190-iskusstvennyj-intellekt-problemy-i-perspektivy.
4. Solntseva OG. Aspects of applying artificial intelligence technologies. E-Management [Internet]. 2018;1. Available from: https://cyberleninka.ru/article/n/aspekty-primeneniya-tehnologiy-iskusstvennogo-intellekta .
5. Sharden B, Massaron L, Bosketti A. Large-scale machine learning with Python: textbook. Moscow: DMK Press; 2018.
