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.
Highlights
S MART manufacturing integrates big data, advanced analytics, high-performance computing, and the industrial Internet of things to manufacturing systems and industries to improve manufacturing processes, resulting in better quality products that are available at lower costs [1]
Machine learning (ML) is considered as an enabling technology for smart manufacturing, which has contributed to the growth of the ‘industry 4.0’ era, resulting in increased research interest in the application of data/predictive analytics, ML, and advanced information and communication technologies for improving manufacturing processes
To empirically evaluate the performance of the 2-DConvLSTMAE model, historical data are used from a bodymaker machine
Summary
S MART manufacturing integrates big data, advanced analytics, high-performance computing, and the industrial Internet of things to manufacturing systems and industries to improve manufacturing processes, resulting in better quality products that are available at lower costs [1]. Machine learning (ML) is considered as an enabling technology for smart manufacturing, which has contributed to the growth of the ‘industry 4.0’ era, resulting in increased research interest in the application of data/predictive analytics, ML, and advanced information and communication technologies for improving manufacturing processes. Leveraging the complementary strengths of CNN and LSTM neural networks, the convolutional LSTM (convLSTM) model both preserves spatial information and performs well in sequential learning [17] Motivated by this modeling approach, this article proposes 2-DConvLSTMAE, a deep ConvLSTM stacked autoencoder for univariate, multistep machine speed forecasting in a manufacturing process.
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