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

Daqian Wei, Bo Wang, Gang Lin, Dichen Liu, Zhaoyang Dong, Hesen Liu and Yilu Liu
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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

Energies, 2017, vol. 10, issue 3, 1-22

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.

Keywords: deep learning; recurrent neural network (RNN); natural language processing (NLP); long short-term memory (LSTM); unstructured data; malfunction inspection report (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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