Your browser does not support JavaScript!

Graduate theses

Current Record: 1 of 6

Back to Results Previous page
Next page
Add to Basket
[Add to Basket]
Identifier 000432644
Title Applying machine learning techniques for predictive maintenance of professional equipment
Alternative Title Εφαρμογές τεχνικών μηχανικής μάθησης για την προληπτική συντήρηση επαγγελματικού εξοπλισμού
Author Σφακιανάκης, Παντελεήμων
Thesis advisor Σμαραγδάκης, Κώστας
Ταρουδάκης, Μιχαήλ
Abstract Predictive maintenance is a concept becoming increasingly popular amongst businesses thanks to the huge economic benefits it provides to them. In this thesis the issue of predictive failure incidents for a water pump will be discussed. Three different Recurent Neural Networks will be compared in order to realise which is more appropriate for this task. These neural networks are: Simple RNN, Long Short Term Memory and Gated Recurrent Unit. Simple RNN uses less controlling knobs than the GRU which uses less than the LSTM.
Language English, Greek
Subject Προγνωστική συντήρηση
Issue date 2020.
Collection   Faculty/Department--Faculty of Sciences and Engineering--Department of Mathematics and Applied Mathematics--Graduate theses
  Type of Work--Graduate theses
Permanent Link Bookmark and Share
Views 17

Digital Documents
No preview available

View document
Views : 11