TITLE:
Business Intelligence and Machine Learning Methods for Predictive Maintenance in Greek railways
AUTHORS:
Ilias Kalathas, Michail Papoutsidakis, Christos Drosos
KEYWORDS:
Business Intelligence, Machine Learning, Data Mining, Decision Making, Rolling Stock, Predictive Maintenance, Weka, KNIME
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.11 No.1A,
June
30,
2021
ABSTRACT: The extraction of useful information supports the process of making business decisions. In every mechanical process, application or service, the periodic maintenance of the necessary equipment is an expensive process and therefore the technicians and the supervisors have the responsibility of the proper decision making. At the railway companies, a huge amount of data is produced which, with the appropriate processing and smart business systems, can attribute quality information and knowledge. In this paper, the benefits of the business intelligence are presented with the techniques of machine learning and data mining involved of the Greek railway companies, which use obsolete procedures of maintenance. In addition, a study of the present situation is held as well as a record of the needs and requirements of the railway companies. At the same time, tools (open source of low cost) of machine learning and data mining are examined that can assist on the creation of a new strategic support of decisions for the development of the predictive maintenance of the Greek railways making a new complete system of business intelligence. Finally, the results and the motives of the railway companies are presented in order to create applications which can constitute the basic tool for the improvement of making decisions by the business’s administration.