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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| Published in: | IEEE transactions on industrial informatics Vol. 16; no. 9; pp. 6069 - 6078 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Piscataway
IEEE
01.09.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1551-3203, 1941-0050 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Giannetti, Cinzia Essien, Aniekan |
| Author_xml | – sequence: 1 givenname: Aniekan orcidid: 0000-0001-9501-0647 surname: Essien fullname: Essien, Aniekan email: a.e.essien@swansea.ac.uk organization: Future Manufacturing Research Institute, College of Engineering, Swansea University, Swansea, UK – sequence: 2 givenname: Cinzia orcidid: 0000-0003-0339-5872 surname: Giannetti fullname: Giannetti, Cinzia email: c.giannetti@swansea.ac.uk organization: Future Manufacturing Research Institute, College of Engineering, Swansea University, Swansea, UK |
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| SubjectTerms | 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 |
| Title | A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders |
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