Tipo di tesi
Tesi di laurea magistrale
Titolo
Data Science for Industry 4.0: Time series analysis for predictive maintenance
Corso di studi
DATA SCIENCE AND BUSINESS INFORMATICS
Parole chiave
- data science
- industry 4.0
- LSTM
- predictive maintenance
- process optimization
- RNN
Data inizio appello
04/10/2019
Consultabilità
Non consultabile
Data di rilascio
04/10/2089
Riassunto (Italiano)
This thesis is about the analysis of a dataset coming from sensors in industrial context, in particular from machineries for leak testing. From more than one year of data collecting, including cases of past failures and normal activity values, we are able to model the conditions of both situations and we want to predict when the next downtime needs to be scheduled, for performing the relative maintenance (predictive maintenance). Since the nature of data, that are sequential and with long term dependencies, we deploy Recurrent Neural Network (RNNs) for modeling such dependencies, in particular the Long Short Term Memories (LSTM). We also propose a comparison with non-recursive models.
