Optimization of the Procedures for Checking the Functionality of the Greek Railways: Data Mining and Machine Learning Approach to Predict Passenger Train Immobilization

Optimization of the Procedures for Checking the Functionality of the Greek Railways: Data Mining and Machine Learning Approach to Predict Passenger Train Immobilization

Volume 5, Issue 4, Page No 287-295, 2020

Author’s Name: Ilias Kalathasa), Michail Papoutsidakis, Chistos Drosos

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Department of Industrial Design and Production Engineering, University of West Attica, Athens, 15354, Greece

a)Author to whom correspondence should be addressed. E-mail: i.kalathas@uniwa.gr

Adv. Sci. Technol. Eng. Syst. J. 5(4), 287-295 (2020); a  DOI: 10.25046/aj050435

Keywords: Machine learning, Railway, Train Immobilization, Data mining, Predictive Model, Malfunctions, Diagnosis

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Abstract

Information is the driving force of businesses because it can ensure the ability of knowledge and prediction. The railway industry produces a huge volume of data, with the proper processing of them and the use of innovative technology, there is the possibility of beneficial information to be provided which constitute the deciding factor for the correct decision making. Safety is the railway comparative advantage that has to be reinforced by each business administration while making the optimum decisions. The main purpose of this paper is the investigation of the most important dysfunctions that arise in a train and can cause its immobilization at the main passenger rail, resulting in huge delays of conducting the routes setting the passengers at risk. Afterwards the total of malfunctions is assessed and the most important, potentially, malfunction is assessed, so as the executives of the Greek Railway company to plan and redefine the processes and the initial plan of the predictive maintenance. This paper demonstrates the effort of implementing innovative applications by making use of methods from the rapidly developed field of Data Mining to the Greek Railway Company that uses obsolete procedures for the control of the trains’ functionality in order to investigate the data for the provision of specialized information which will be used as a tool for the faster, more accurate and precise decision making. This decision making approach is based on a specific algorithm’s design in order to automatically detect faults and make periodic maintenance of trains easier. Holistic approach is performed in the management of real data from the Greek railway industry and a predictive model of Machine Learning is developed, for the optimization of the management’s performance of the trains reinforcing the strategic target of the railway industry which is the transportation of citizens with safety and comfort.

Received: 17 June 2020, Accepted: 19 July 2020, Published Online: 28 July 2020

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