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Home » On the Use of LSTM Networks for Predictive Maintenance in Smart Industries

On the Use of LSTM Networks for Predictive Maintenance in Smart Industries

TitleOn the Use of LSTM Networks for Predictive Maintenance in Smart Industries
Publication TypeConference Paper
Year of Publication2019
AuthorsBruneo, D.., and F.. De Vita
Conference Name2019 IEEE International Conference on Smart Computing (SMARTCOMP)
Date PublishedJune
KeywordsDeep Learning, Engines, History, Industry 4.0, Keras, learning (artificial intelligence), Logic gates, LSTM networks hyperparameters, Machine learning, machine learning approach, machine learning techniques, maintenance engineering, maintenance scheduling, predictive maintenance, production engineering computing, recurrent neural nets, remaining life assessment, remaining useful life, RUL, Sensor systems, smart industries, Smart Industry, TensorFlow
DOI10.1109/SMARTCOMP.2019.00059
Tags: 
learning (artificial intelligence)
maintenance engineering
production engineering computing
recurrent neural nets
remaining life assessment
remaining useful life
smart industries
LSTM networks hyperparameters
machine learning approach
RUL
machine learning techniques
maintenance scheduling
predictive maintenance
Engines
Logic gates
Maintenance engineering
Machine learning
Sensor systems
History
Predictive Maintenance
Deep Learning
TensorFlow
Keras
Smart Industry
Industry 4.0
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The #SmartME project was born from a wish of a team of researchers in the Mobile and Distributed Systems Lab (MDSLab) at the University of Messina who, in collaboration with the Industrial Liaison Office and the Center for Information Services of the University (CIAM), are eager to encourage, in an innovative fashion, a “conversation” with the municipality of Messina,  ... read more.

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