dc.contributor.author | Τζουμέζη, Βασιλική | el |
dc.contributor.author | Tzoumezi, Vasiliki | en |
dc.date.accessioned | 2020-11-18T08:46:47Z | |
dc.date.available | 2020-11-18T08:46:47Z | |
dc.identifier.uri | https://dspace.lib.ntua.gr/xmlui/handle/123456789/51932 | |
dc.identifier.uri | http://dx.doi.org/10.26240/heal.ntua.19630 | |
dc.rights | Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/gr/ | * |
dc.subject | Νεωρωνικά δίκτυα | el |
dc.subject | Ναυτικοί κινητήρες ντίζελ | el |
dc.subject | Ναυτική μηχανολογία | el |
dc.subject | Neural networks | en |
dc.subject | Marine powertrain | en |
dc.subject | Virtual sensors | en |
dc.subject | Εκτίμηση παραμέτρων μηχανής πρόωσης | el |
dc.subject | Diesel engine parameters | en |
dc.subject | Μηχανική μάθηση | el |
dc.subject | Machine learning | en |
dc.title | Parameters estimation in marine powertrain using neural networks | en |
heal.type | bachelorThesis | |
heal.classification | Marine Engineering | en |
heal.classification | Ναυτική Μηχανολογία | el |
heal.language | en | |
heal.access | free | |
heal.recordProvider | ntua | el |
heal.publicationDate | 2020-11-05 | |
heal.abstract | There is a wide range of engine performance parameters affecting the combustion process, while some of them have been considered of utmost importance for emission modeling and engine control issues. The Fuel Consumption, MAP, λ and NOx are part of the most indicative engine variables and have stimulated interest in this work. In fact, Artificial Neural Network (ANN) models for predicting the aforementioned quantities could replace the real sensors, which sometimes might be unable to perform direct measurements or to provide with trustworthy values, and on top of that could be quite expensive. These models are known as the neural network-based virtual sensors which could be implemented on-board and, through strong generalization performance, provide results with high accuracy. For this work, the Feed-Forward Neural Network (FFNN) and Time-Delay Neural Network (TDNN) architectures are investigated for the predictive models, and their results are accordingly compared. The networks are trained, validated and tested using experimental data collected from various trials on the laboratory test-bed. In addition, a fully parametric study have been conducted concerning the models inputs selection. The latter has been based on: the theoretical background of the engine function, the relationship between various engine parameters and the available quantities measured by real sensors. Of course, with regard to the acquired experience through modeling, both the inputs and the calculation mechanisms of the models were revised until achieving the most efficient performance. After that, the models were tested on data sets within the same range of the training set (i.e. the whole envelope of test-bed) and on completely unknown data, with different pattern and scaling. Both types of deep neural network models performed well, with no appreciable errors. For the whole modeling process the Python language was used and mainly the Keras library (interface of Tensorflow 2.0), except for the Data Preparation process which was completed in the MATLAB environment. | en |
heal.advisorName | Παπαλάμπρου, Γεώργιος | el |
heal.advisorName | Papalambrou, George | en |
heal.committeeMemberName | Κυρτάτος, Νικόλαος | el |
heal.committeeMemberName | Kyrtatos, Nikolaos | en |
heal.committeeMemberName | Γρηγορόπουλος, Γρηγόρης | el |
heal.committeeMemberName | Grigoropoulos, Gregory | en |
heal.academicPublisher | Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ναυπηγών Μηχανολόγων Μηχανικών. Τομέας Ναυτικής Μηχανολογίας | el |
heal.academicPublisherID | ntua | |
heal.numberOfPages | 120 σ. | el |
heal.fullTextAvailability | false |
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