A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders

Time-series forecasting is applied to many areas of smart factories, including machine health monitoring, predictive maintenance, and production scheduling. In smart factories, machine speed prediction can be used to dynamically adjust production processes based on different system conditions, optim...

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Veröffentlicht in:	IEEE transactions on industrial informatics Jg. 16; H. 9; S. 6069 - 6078
Hauptverfasser:	Essien, Aniekan, Giannetti, Cinzia
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.09.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Coders Convolution Convolutional long short-term memory (ConvLSTM) Deep learning deep learning (DL) Empirical analysis Energy conservation Energy consumption Factories Forecasting Industrial plants industry 4.0 Logic gates Machine learning Manufacturing Manufacturing processes Mathematical models Neural networks Noise prediction Prediction models Predictive maintenance Predictive models Production scheduling Smart manufacturing Spatial distribution stacked autoencoders time-series forecasting
ISSN:	1551-3203, 1941-0050
Online-Zugang:	Volltext
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