Vehicle maintenance management based on machine learning in agricultural tractor engines
| Autor: | Carlos Nolasco Mafla-Yépez, Cesar Fabricio Morales-Bayetero, Erik Paul Hernández-Rueda, Ignacio Bayardo Benavides-Cevallos |
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| Jazyk: | English<br />Spanish; Castilian |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Dyna, Vol 90, Iss 225 (2023) |
| Druh dokumentu: | article |
| ISSN: | 0012-7353 2346-2183 |
| DOI: | 10.15446/dyna.v90n225.103612 |
| Popis: | The objective of this work is to use the autonomous learning methodology as a tool in vehicle maintenance management. In obtaining data, faults in the fuel supply system have been simulated, causing anomalies in the combustion process that are easily detectable by vibrations obtained from a sensor in the engine of an agricultural tractor. To train the classification algorithm, 4 engine states were used: BE (optimal state), MEF1, MEF2, MEF3 (simulated failures). The applied autonomous learning is of the supervised type, where the samples were initially characterized and labeled to create a database for the execution of the training. The results show that the training carried out within the classification algorithm has an efficiency greater than 90%, which indicates that the method used is applicable in the management of vehicle maintenance to predict failures in engine operation. |
| Databáze: | Directory of Open Access Journals |
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