Abstract
The unsupervised dynamic models have been applied to various tasks in the process industry due to their excellent ability to represent the process dynamics. The recurrent-network-based dynamic feature extractor is a typical unsupervised dynamic model which extracts the dynamic data features using a recurrent encoder network. However, the recurrent-network-based dynamic feature extractor has low computational efficiency due to its recurrent nature, which prevents the model from being used for large-scale data sets. To improve computational efficiency, a new dynamic feature extractor called TempoATTNE-DFE is proposed in this work. In TempoATTNE-DFE, a new encoder structure is developed, which can be implemented in parallel for data sequences. Meanwhile, a kind of attention mechanism is proposed to extract the dynamic features within the input sequence. The proposed TempoATTNE-DFE can achieve higher computational efficiency in offline training and online inference. To evaluate the effectiveness of TempoATTNE-DFE, it is applied to the quality prediction task and validated with a numerical example and two real-world industrial processes. The application results demonstrate that TempoATTNE-DFE can achieve better prediction performance compared to other state-of-the-art methods. In addition, compared with the recurrent-network-based dynamic feature extractor, TempoATTNE-DFE gains <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ 1.29\times$</tex-math></inline-formula> speedup in training and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ 2.45\times$</tex-math></inline-formula> speedup in inference on the blast furnace data set.
Published Version
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