Description

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.