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Locomotive fault prognosis via machine learning

dc.contributor.advisorNikolaou , Grigoris
dc.contributor.authorΓεωργαλής, Χαράλαμπος
dc.date.accessioned2021-03-12T09:45:27Z
dc.date.available2021-03-12T09:45:27Z
dc.date.issued2021-02-19
dc.identifier.urihttps://polynoe.lib.uniwa.gr/xmlui/handle/11400/387
dc.identifier.urihttp://dx.doi.org/10.26265/polynoe-238
dc.description.abstractRailway has played a vital role in transportation of both goods and people historically, continuing to hold an important share of the market with great potentials. Locomotives are the main part of a train and as with any mechanism; maintenance and troubleshooting are of critical importance. Except for corrective and preventing maintenance, the new trend in all industries is the fault prognostics, also known as predictive maintenance, whose goal is to detect an upcoming breakdown. Almost every mechanical compartment uses some type of bearings. So, this research focus on two main pillars, the first part is about to review the available technical manual and to count the bearings used in locomotives, while the second part is about to construct a deep machine learning model for bearings fault diagnosis and prognosis based on secondary data.el
dc.format.extent172el
dc.language.isoenel
dc.publisherΠανεπιστήμιο Δυτικής Αττικήςel
dc.publisherΠανεπιστήμιο Αιγαίουel
dc.rightsΑναφορά Δημιουργού - Μη Εμπορική Χρήση - Παρόμοια Διανομή 4.0 Διεθνές*
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Διεθνές*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectBearingsel
dc.subjectBiLSTMel
dc.subjectK-meansel
dc.subjectLocomotivesel
dc.subjectModellingel
dc.subjectPrognosticsel
dc.subjectRegressionel
dc.subjectVibrationsel
dc.subjectCondition-based maintenanceel
dc.subjectDeep learningel
dc.subjectFault prognosisel
dc.subjectGenetic algorithmsel
dc.subjectLinear regressionel
dc.subjectMulti-class classificationel
dc.subjectPredictive maintenanceel
dc.subjectRolling stockel
dc.subjectSignal processingel
dc.subjectSupervised machine learningel
dc.subjectΜηχανική μάθησηel
dc.subjectΓενετικοί αλγόριθμοιel
dc.subjectΔιάγνωση σφαλμάτωνel
dc.subjectΡουλεμάνel
dc.subjectΠρόγνωση σφαλμάτωνel
dc.subjectΠρογνωστική συντήρησηel
dc.titleLocomotive fault prognosis via machine learningel
dc.title.alternativeΠρόγνωση βλαβών κινητήριων μονάδων τροχαίου υλικού με μηχανική μάθησηel
dc.typeΜεταπτυχιακή διπλωματική εργασίαel
dc.contributor.committeeΠαπουτσιδάκης, Μιχαήλ
dc.contributor.committeeDrosos, Christos
dc.contributor.facultyΣχολή Μηχανικώνel
dc.contributor.departmentΤμήμα Μηχανικών Βιομηχανικής Σχεδίασης και Παραγωγήςel
dc.contributor.masterΝέες Τεχνολογίες στη Ναυτιλία και τις Μεταφορέςel


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Αναφορά Δημιουργού - Μη Εμπορική Χρήση - Παρόμοια Διανομή 4.0 Διεθνές
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Αναφορά Δημιουργού - Μη Εμπορική Χρήση - Παρόμοια Διανομή 4.0 Διεθνές