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dc.contributor.authorCastresana, Joseba-
dc.contributor.authorGabina, Gorka-
dc.contributor.authorMartin, Leopoldo; Basterretxea, Aingeru-
dc.contributor.authorUriondo, Zigor-
dc.date.accessioned2023-10-04T10:45:02Z-
dc.date.available2023-10-04T10:45:02Z-
dc.date.issued2022-
dc.identifierWOS:000783233800009-
dc.identifier.issn0016-2361-
dc.identifier.urihttp://dspace.azti.es/handle/24689/1567-
dc.description.abstractMarine incidents given in recent years have been in part caused by propulsion issues. In this context, incipient propulsion faults may be identified by deviations between real values and healthy engine values provided by an accurate model. Engine modelling techniques have thus become a topic of interest in the last decade. On this basis, Machine learning approaches such as Artificial Neural Networks (ANN) have proved to be accurate and fast in terms of calculation times. However, up to now most research work has focused on predicting a few parameters for specific operation points. In order to analyse the generalization capability of ANN when predicting multiple outputs in real engine conditions, 35 different performance and emission parameters were simultaneously predicted in this study with an ANN. To do so, different engine operation points were tested in a sixcylinder marine diesel engine, characterizing the whole engine performance map. Additionally, some points from random regions throughout the entire engine performance map were tested to later analyse ANN performance on them. After defining network optimum structure and training and validating the Artificial Neural Network with 1000 data samples, the ANN was tested with data extracted from unseen random regions of the performance map. Mean Absolute Percentage Errors obtained for testing samples from random points of the engine performance map remained below 8.5\% for all parameters with the exception of CO and NO2 emissions predictions. For low temperature and high temperature cooling systems, oil system and exhaust gas system, MAPE values obtained were below 4.3\%. Calculation time for 24 testing samples containing 35 parameters was 0.109 s, which along with the high accuracy level obtained demonstrated that ANN can predict multiple outputs throughout the whole engine performance map.-
dc.language.isoEnglish-
dc.publisherELSEVIER SCI LTD-
dc.subjectANN-
dc.subjectDiesel engine modelling-
dc.subjectPerformance prediction-
dc.subjectEmission prediction-
dc.subjectPerformance map modelling-
dc.subjectMultiple output prediction-
dc.subjectARTIFICIAL NEURAL-NETWORK-
dc.subjectEMISSION CHARACTERISTICS-
dc.subjectFAULT-DETECTION-
dc.subjectPREDICTION-
dc.subjectFUEL-
dc.subjectSIMULATION-
dc.subjectMAINTENANCE-
dc.subjectCOMBUSTION-
dc.subjectPRESSURE-
dc.subjectSYSTEM-
dc.titleMarine diesel engine ANN modelling with multiple output for complete engine performance map-
dc.typeArticle-
dc.identifier.journalFUEL-
dc.format.volume319-
dc.contributor.funderBasque Government-
dc.contributor.funderDepartment of Economic Development and Infrastructures-
dc.identifier.e-issn1873-7153-
dc.identifier.doi10.1016/j.fuel.2022.123873-
Appears in Publication types:Artículos científicos



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