Journal article 1019 views 389 downloads
A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders
IEEE Transactions on Industrial Informatics, Volume: 16, Issue: 9, Pages: 6069 - 6078
Swansea University Authors:
Aniekan Essien , Cinzia Giannetti
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DOI (Published version): 10.1109/tii.2020.2967556
Abstract
Timeseries forecasting is applied to many areas of smart factories, including machine health monitoring (MHM), predictive maintenance, and production scheduling. In smart factories, machine speed prediction can be used to dynamically adjust production processes based on different system conditions,...
Published in: | IEEE Transactions on Industrial Informatics |
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ISSN: | 1551-3203 1941-0050 |
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Institute of Electrical and Electronics Engineers (IEEE)
2020
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URI: | https://cronfa.swan.ac.uk/Record/cronfa53318 |
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2021-08-05T15:42:16.9287367 v2 53318 2020-01-21 A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders 8fee3cc958c5121489b2575535864ae6 0000-0001-9501-0647 Aniekan Essien Aniekan Essien true false a8d947a38cb58a8d2dfe6f50cb7eb1c6 0000-0003-0339-5872 Cinzia Giannetti Cinzia Giannetti true false 2020-01-21 EEN Timeseries forecasting is applied to many areas of smart factories, including machine health monitoring (MHM), predictive maintenance, and production scheduling. In smart factories, machine speed prediction can be used to dynamically adjust production processes based on different system conditions, optimise production throughput, and minimise energy consumption. However, making accurate data-driven machine speed forecasts is challenging. Given the complex nature of industrial manufacturing processes, predictive models that are robust to noise and can capture the temporal and spatial distributions of the input timeseries are prerequisites for accurate forecasting. Motivated by recent deep learning studies in smart manufacturing, this paper proposes an end-to-end model for multi-step-ahead machine speed prediction. The model, known as 2D-Convolutional LSTM Autoencoder (2DConvLSTMAE), comprises a deep convolutional LSTM (ConvLSTM) 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 to state-of-the-art deep learning models. Journal Article IEEE Transactions on Industrial Informatics 16 9 6069 6078 Institute of Electrical and Electronics Engineers (IEEE) 1551-3203 1941-0050 deep learning, ConvLSTM, stacked autoencoders, timeseries prediction, Industry 4.0 1 9 2020 2020-09-01 10.1109/tii.2020.2967556 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University UKRI, EP/S001387/1 2021-08-05T15:42:16.9287367 2020-01-21T10:14:33.2458190 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Aniekan Essien 0000-0001-9501-0647 1 Cinzia Giannetti 0000-0003-0339-5872 2 53318__17414__2da379d11c2f437aa113e87ad1ed306b.pdf 53318.pdf 2020-06-03T16:26:37.7735253 Output 5125326 application/pdf Version of Record true true https://creativecommons.org/licenses/by/4.0/ |
title |
A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders |
spellingShingle |
A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders Aniekan Essien Cinzia Giannetti |
title_short |
A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders |
title_full |
A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders |
title_fullStr |
A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders |
title_full_unstemmed |
A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders |
title_sort |
A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders |
author_id_str_mv |
8fee3cc958c5121489b2575535864ae6 a8d947a38cb58a8d2dfe6f50cb7eb1c6 |
author_id_fullname_str_mv |
8fee3cc958c5121489b2575535864ae6_***_Aniekan Essien a8d947a38cb58a8d2dfe6f50cb7eb1c6_***_Cinzia Giannetti |
author |
Aniekan Essien Cinzia Giannetti |
author2 |
Aniekan Essien Cinzia Giannetti |
format |
Journal article |
container_title |
IEEE Transactions on Industrial Informatics |
container_volume |
16 |
container_issue |
9 |
container_start_page |
6069 |
publishDate |
2020 |
institution |
Swansea University |
issn |
1551-3203 1941-0050 |
doi_str_mv |
10.1109/tii.2020.2967556 |
publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
college_str |
Faculty of Science and Engineering |
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|
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facultyofscienceandengineering |
hierarchy_top_title |
Faculty of Science and Engineering |
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facultyofscienceandengineering |
hierarchy_parent_title |
Faculty of Science and Engineering |
department_str |
School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering |
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description |
Timeseries forecasting is applied to many areas of smart factories, including machine health monitoring (MHM), predictive maintenance, and production scheduling. In smart factories, machine speed prediction can be used to dynamically adjust production processes based on different system conditions, optimise production throughput, and minimise energy consumption. However, making accurate data-driven machine speed forecasts is challenging. Given the complex nature of industrial manufacturing processes, predictive models that are robust to noise and can capture the temporal and spatial distributions of the input timeseries are prerequisites for accurate forecasting. Motivated by recent deep learning studies in smart manufacturing, this paper proposes an end-to-end model for multi-step-ahead machine speed prediction. The model, known as 2D-Convolutional LSTM Autoencoder (2DConvLSTMAE), comprises a deep convolutional LSTM (ConvLSTM) 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 to state-of-the-art deep learning models. |
published_date |
2020-09-01T04:06:12Z |
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1763753457484824576 |
score |
11.021648 |