Predictive Maintenance Using Machine Learning and Data Mining: A Pioneer Method Implemented to Greek Railways
In every business, the production of knowledge, coming from the process of effective information, is recognized as a strategic asset and source of competitive advantage. In the field of railways, a vast amount of data are produced, which is necessary to be assessed, deployed in an optimum way, and u...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Designs |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2411-9660/5/1/5 |
| _version_ | 1797411269570985984 |
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| author | Ilias Kalathas Michail Papoutsidakis |
| author_facet | Ilias Kalathas Michail Papoutsidakis |
| author_sort | Ilias Kalathas |
| collection | DOAJ |
| description | In every business, the production of knowledge, coming from the process of effective information, is recognized as a strategic asset and source of competitive advantage. In the field of railways, a vast amount of data are produced, which is necessary to be assessed, deployed in an optimum way, and used as a mechanism, which will lead to making the right decisions, aiming at saving resources and maintain the fundamental principle of the railways which is the passengers’ safety. This paper uses stored-inactive data from a Greek railway company, and uses the method of data mining and applies machine learning techniques to create strategic decision support and draw up a risk and control plan for trains. We make an effort to apply Machine Learning open source software (Weka) to the obsolete procedures of maintenance of the rolling stock of the company (hand-written work orders from the supervisors to the technicians, dealing with the dysfunctions of a train unit by experience, the lack of planning and coding of the malfunctions and the maintenance schedule). Using the J48 and M5P algorithms from the Weka software, data are recorded, processed, and analyzed that can help monitor or discover, with great accuracy, the prevention of possible damage or stresses, without the addition of new recording devices—monitoring on trains, with the aim of predicting the diagnosis of the train fleet. The innovative method is capable of being used as a tool for the optimization of the management’s performance of the trains to provide the appropriate information for the implementation of planning and the technical ability of the trains in order to achieve the greatest target of importance for the railways, which is the passengers’ safety. |
| first_indexed | 2024-03-09T04:43:37Z |
| format | Article |
| id | doaj.art-151c780dc8e847759147aa534cafdb49 |
| institution | Directory Open Access Journal |
| issn | 2411-9660 |
| language | English |
| last_indexed | 2024-03-09T04:43:37Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Designs |
| spelling | doaj.art-151c780dc8e847759147aa534cafdb492023-12-03T13:17:44ZengMDPI AGDesigns2411-96602021-01-0151510.3390/designs5010005Predictive Maintenance Using Machine Learning and Data Mining: A Pioneer Method Implemented to Greek RailwaysIlias Kalathas0Michail Papoutsidakis1Department of Industrial Design and Production Engineering, University of West Attica, 15354 Athens, GreeceDepartment of Industrial Design and Production Engineering, University of West Attica, 15354 Athens, GreeceIn every business, the production of knowledge, coming from the process of effective information, is recognized as a strategic asset and source of competitive advantage. In the field of railways, a vast amount of data are produced, which is necessary to be assessed, deployed in an optimum way, and used as a mechanism, which will lead to making the right decisions, aiming at saving resources and maintain the fundamental principle of the railways which is the passengers’ safety. This paper uses stored-inactive data from a Greek railway company, and uses the method of data mining and applies machine learning techniques to create strategic decision support and draw up a risk and control plan for trains. We make an effort to apply Machine Learning open source software (Weka) to the obsolete procedures of maintenance of the rolling stock of the company (hand-written work orders from the supervisors to the technicians, dealing with the dysfunctions of a train unit by experience, the lack of planning and coding of the malfunctions and the maintenance schedule). Using the J48 and M5P algorithms from the Weka software, data are recorded, processed, and analyzed that can help monitor or discover, with great accuracy, the prevention of possible damage or stresses, without the addition of new recording devices—monitoring on trains, with the aim of predicting the diagnosis of the train fleet. The innovative method is capable of being used as a tool for the optimization of the management’s performance of the trains to provide the appropriate information for the implementation of planning and the technical ability of the trains in order to achieve the greatest target of importance for the railways, which is the passengers’ safety.https://www.mdpi.com/2411-9660/5/1/5machine learningdata miningpredictive maintenancedecision supportWeka |
| spellingShingle | Ilias Kalathas Michail Papoutsidakis Predictive Maintenance Using Machine Learning and Data Mining: A Pioneer Method Implemented to Greek Railways Designs machine learning data mining predictive maintenance decision support Weka |
| title | Predictive Maintenance Using Machine Learning and Data Mining: A Pioneer Method Implemented to Greek Railways |
| title_full | Predictive Maintenance Using Machine Learning and Data Mining: A Pioneer Method Implemented to Greek Railways |
| title_fullStr | Predictive Maintenance Using Machine Learning and Data Mining: A Pioneer Method Implemented to Greek Railways |
| title_full_unstemmed | Predictive Maintenance Using Machine Learning and Data Mining: A Pioneer Method Implemented to Greek Railways |
| title_short | Predictive Maintenance Using Machine Learning and Data Mining: A Pioneer Method Implemented to Greek Railways |
| title_sort | predictive maintenance using machine learning and data mining a pioneer method implemented to greek railways |
| topic | machine learning data mining predictive maintenance decision support Weka |
| url | https://www.mdpi.com/2411-9660/5/1/5 |
| work_keys_str_mv | AT iliaskalathas predictivemaintenanceusingmachinelearninganddataminingapioneermethodimplementedtogreekrailways AT michailpapoutsidakis predictivemaintenanceusingmachinelearninganddataminingapioneermethodimplementedtogreekrailways |