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Product quality time series prediction with attention-based convolutional recurrent neural network

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Abstract

The product quality is the key index to measure the process of the industrial manufacture. Thanks to the ever-expanding scale of time-series data, the deep learning technology can be regarded as the effective approach to predict the future product quality accurately. In this article, the product quality with time series data is considered and an attention-based convolutional recurrent neural network (ACRNN) is proposed for the prediction of the product quality. Firstly, by reconstructing the time series data into the two dimensions the convolutional layers are built to compress the information of the product quality data, and the more comprehensive features can be extracted. Furthermore, to ensure the prediction accuracy of the time series data process and extract the 2-dimmension feature of the data, the long short-term memory (LSTM) based on recurrent neural network (RNN) layers are constructed. After that, the fully connection layers with attention mechanism is applied to improve the calculation efficiency and accuracy of the models. Finally, the test experiments on the time series data of the industrial product are given and the comparisons show that the effectiveness of the proposed algorithm framework.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work is supported by the National Nature Science Foundation of China under Grant (61973331).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China

    Yiguan Shi

  2. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Yong Chen & Longjie Zhang

  3. China South Industries Group Automation Research Institute Co. Ltd, Mianyang, 621000, China

    Yiguan Shi

Authors
  1. Yiguan Shi
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  2. Yong Chen
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  3. Longjie Zhang
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Contributions

Yiguan Shi, Yong Chen and Longjie Zhang contributed to the conception of the presented idea. Yiguan Shi and Longjie Zhang wrote the main text, performed the simulations and prepared all figures. Yong Chen revised the manuscript. All authors reviewed the manuscript.

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Correspondence to Yong Chen.

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Cite this article

Shi, Y., Chen, Y. & Zhang, L. Product quality time series prediction with attention-based convolutional recurrent neural network. Appl Intell 54, 10763–10779 (2024). https://doi.org/10.1007/s10489-024-05709-2

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