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Research on Unstructured Text Data Mining and Fault Classification Based on RNN-LSTM with Malfunction Inspection Report

Author

Listed:
  • Daqian Wei

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Bo Wang

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Gang Lin

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Dichen Liu

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Zhaoyang Dong

    (School of Electrical Engineering and Telecommunications, University of NSW, Sydney 2052, Australia)

  • Hesen Liu

    (Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA)

  • Yilu Liu

    (Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA)

Abstract

This paper documents the condition-based maintenance (CBM) of power transformers, the analysis of which relies on two basic data groups: structured (e.g., numeric and categorical) and unstructured (e.g., natural language text narratives) which accounts for 80% of data required. However, unstructured data comprised of malfunction inspection reports, as recorded by operation and maintenance of the power grid, constitutes an abundant untapped source of power insights. This paper proposes a method for malfunction inspection report processing by deep learning, which combines the text data mining–oriented recurrent neural networks (RNN) with long short-term memory (LSTM). In this paper, the effectiveness of the RNN-LSTM network for modeling inspection data is established with a straightforward training strategy in which we replicate targets at each sequence step. Then, the corresponding fault labels are given in datasets, in order to calculate the accuracy of fault classification by comparison with the original data labels and output samples. Experimental results can reflect how key parameters may be selected in the configuration of the key variables to achieve optimal results. The accuracy of the fault recognition demonstrates that the method we proposed can provide a more effective way for grid inspection personnel to deal with unstructured data.

Suggested Citation

  • Daqian Wei & Bo Wang & Gang Lin & Dichen Liu & Zhaoyang Dong & Hesen Liu & Yilu Liu, 2017. "Research on Unstructured Text Data Mining and Fault Classification Based on RNN-LSTM with Malfunction Inspection Report," Energies, MDPI, vol. 10(3), pages 1-22, March.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:3:p:406-:d:93644
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    References listed on IDEAS

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    1. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
    2. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 4.
    3. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 3.
    4. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 1.
    5. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 4.
    6. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 3.
    7. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
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    Cited by:

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    2. Yixing Wang & Meiqin Liu & Zhejing Bao & Senlin Zhang, 2018. "Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks," Energies, MDPI, vol. 11(5), pages 1-19, May.
    3. Hongchen Li & Zhong Yang & Jiaming Han & Shangxiang Lai & Qiuyan Zhang & Chi Zhang & Qianhui Fang & Guoxiong Hu, 2020. "TL-Net: A Novel Network for Transmission Line Scenes Classification," Energies, MDPI, vol. 13(15), pages 1-15, July.
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