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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Vydané v:IEEE transactions on industrial informatics Ročník 16; číslo 9; s. 6069 - 6078
Hlavní autori: Essien, Aniekan, Giannetti, Cinzia
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.09.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Popis
Shrnutí: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, optimize production throughput, and minimize energy consumption. However, making accurate data-driven machine speed forecasts is challenging. Given the complex nature of industrial manufacturing process data, predictive models that are robust to noise and can capture the temporal and spatial distributions of input time-series signals are prerequisites for accurate forecasting. Motivated by recent deep learning studies in smart manufacturing, in this article, we propose an end-to-end model for multistep machine speed prediction. The model comprises a deep convolutional LSTM encoder-decoder architecture. Extensive empirical analyses using real-world data obtained from a metal packaging plant in the United Kingdom demonstrate the value of the proposed method when compared with the state-of-the-art predictive models.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2020.2967556