Design and implementation of a Recurrent Neural Network for Remaining Useful Life prediction

Mohammadisohrabi, Ali (2020) Design and implementation of a Recurrent Neural Network for Remaining Useful Life prediction. [Laurea magistrale], Università di Bologna, Corso di Studio in Automation engineering / ingegneria dell’automazione [LM-DM270], Documento full-text non disponibile
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A key idea underlying many Predictive Maintenance solutions is Remaining Useful Life (RUL) of machine parts, and it simply involves a prediction on the time remaining before a machine part is likely to require repair or replacement. Nowadays, with respect to fact that the systems are getting more complex, the innovative Machine Learning and Deep Learning algorithms can be deployed to study the more sophisticated correlations in complex systems. The exponential increase in both data accumulation and processing power make the Deep Learning algorithms more desirable that before. In this paper a Long Short-Term Memory (LSTM) which is a Recurrent Neural Network is designed to predict the Remaining Useful Life (RUL) of Turbofan Engines. The dataset is taken from NASA data repository. Finally, the performance obtained by RNN is compared to the best Machine Learning algorithm for the dataset.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Mohammadisohrabi, Ali
Relatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
Artificial Neural Networks (ANNs),Deep Learning Algorithms,Long Short-Term Memory (LSTM),Machine Learning Algorithms,Predictive maintenance,prognostics,Reliability,Remaining Useful Life (RUL),Recurrent Neural Networks (RNNs),Survival Analysis.
Data di discussione della Tesi
3 Dicembre 2020

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