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Two-view LSTM variational auto-encoder for fault detection and diagnosis in multivariable manufacturing processes

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Abstract

Process monitoring of industrial production has always been one of the main concerns of process industry systems. As artificial intelligence booms, fault detection and diagnosis via deep learning has been widely used in industrial process monitoring, such as chemical process monitoring flow. Whereas, the current deep framework is difficult to simultaneously reconcile the relationship between different variables and time series caused by the increasing complexity of industrial systems, which reduces the accuracy of fault detection and diagnosis. Considering the significant time series processing potential of long short-term memory (LSTM) networks, this paper proposes a novel two-view LSTM variational auto-encoder, aliased as TVAE. First, the running data in period T is obtained through sliding window, then it utilizes two-view embedding to compress time series information and capture the correlation between time variables, after that it is judged whether it is fault data according to the anomaly score of the variational auto-encoder. Finally, since the encoding process makes the time-dependent and variable dependent features more obvious, the fault characteristic of the encoded data can be obtained by neuron grouping convolution, and then classified for diagnosis. Experimental results on Tennessee Eastman process benchmark show that TVAE is superior to the state-of-the-art methods and provides a new paradigm for industrial process fault detection and diagnosis.

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Data availability

The datasets analysed during the current study are available in the Tennessee Eastman Challenge Archive, http://depts.washington.edu/control/LARRY/TE/download.html.

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Acknowledgements

This work was partially supported by the National Science Foundation of China (62271293, 61903238), the Natural Science Foundation of Shandong Province, PR China (ZR2021MF035), the Social Science Planning Project of Shandong Province, PR China (22CYYJ13).

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250353, Shandong, China

    Li Qi & Yuwei Ren

  2. School of Information Engineering, Shandong Management University, Jinan, 250353, Shandong, China

    Yixian Fang

  3. The College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China

    Jinglin Zhou

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  1. Li Qi
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Correspondence to Yuwei Ren or Yixian Fang.

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Qi, L., Ren, Y., Fang, Y. et al. Two-view LSTM variational auto-encoder for fault detection and diagnosis in multivariable manufacturing processes. Neural Comput & Applic 35, 22007–22026 (2023). https://doi.org/10.1007/s00521-023-08949-4

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