HEAL DSpace

Parameters estimation in marine powertrain using neural networks

Αποθετήριο DSpace/Manakin

Εμφάνιση απλής εγγραφής

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


Αρχεία σε αυτό το τεκμήριο

Οι παρακάτω άδειες σχετίζονται με αυτό το τεκμήριο:

Αυτό το τεκμήριο εμφανίζεται στην ακόλουθη συλλογή(ές)

Εμφάνιση απλής εγγραφής

Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα Εκτός από όπου ορίζεται κάτι διαφορετικό, αυτή η άδεια περιγράφεται ως Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα