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A Deep Learning model for Smart Manufacturing using Convolutional LSTM Neural Network Autoencoders / Aniekan, Essien; Cinzia, Giannetti

IEEE Transactions on Industrial Informatics, Volume: 16, Issue: 9, Pages: 6069 - 6078

Swansea University Authors: Aniekan, Essien, Cinzia, Giannetti

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,...

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Published in: IEEE Transactions on Industrial Informatics
ISSN: 1551-3203 1941-0050
Published: Institute of Electrical and Electronics Engineers (IEEE) 2020
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa53318
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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, 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.
Keywords: deep learning, ConvLSTM, stacked autoencoders, timeseries prediction, Industry 4.0
Issue: 9
Start Page: 6069
End Page: 6078