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

Wei, Daqian; Wang, Bo; Lin, Gang; Liu, Dichen; Dong, Zhaoyang; Liu, Hesen; Li, Fangxing

doi:10.3390/en10030406

Cited by 65 publications

(27 citation statements)

References 26 publications

(23 reference statements)

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“…In the field of NLP, most of the mainstream text classification algorithms are based on convolutional neural network (CNN) and recurrent neural network (RNN) [26][27][28][29]. As a variant of RNN, long short-term memory (LSTM) solves the problem of the long-term dependence of the RNN model and the problem of gradient disappearance and explosion caused by a too long sequence.…”

Section: Network Architecture Of Lstmmentioning

confidence: 99%

Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM

et al. 2020

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In the process of the operation and maintenance of secondary devices in smart substation, a wealth of defect texts containing the state information of the equipment is generated. Aiming to overcome the low efficiency and low accuracy problems of artificial power text classification and mining, combined with the characteristics of power equipment defect texts, a defect texts mining method for a secondary device in a smart substation is proposed, which integrates global vectors for word representation (GloVe) method and attention-based bidirectional long short-term memory (BiLSTM-Attention) method in one model. First, the characteristics of the defect texts are analyzed and preprocessed to improve the quality of the defect texts. Then, defect texts are segmented into words, and the words are mapped to the high-dimensional feature space based on the global vectors for word representation (GloVe) model to form distributed word vectors. Finally, a text classification model based on BiLSTM-Attention was proposed to classify the defect texts of a secondary device. Precision, Recall and F1-score are selected as evaluation indicators, and compared with traditional machine learning and deep learning models. The analysis of a case study shows that the BiLSTM-Attention model has better performance and can achieve the intelligent, accurate and efficient classification of secondary device defect texts. It can assist the operation and maintenance personnel to make scientific maintenance decisions on a secondary device and improve the level of intelligent management of equipment.

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Section: Network Architecture Of Lstmmentioning

confidence: 99%

Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM

et al. 2020

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“…The area under the ROC curve (AUC) is equal to the probability that the classifier ranks a randomly chosen positive instance higher than a randomly chosen negative one. We evaluate the classification performance of each network by the accuracy of test data and the AUC value, as suggested in [7,31]. The larger the AUC value is, the better the classification performance of the network is.…”

Section: Ablation Studiesmentioning

confidence: 99%

TL-Net: A Novel Network for Transmission Line Scenes Classification

Yang

Han

et al. 2020

Energies

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With the development of unmanned aerial vehicle (UAV) control technology, one of the recent trends in this research domain is to utilize UAVs to perform non-contact transmission line inspection. The RGB camera mounted on UAVs collects large numbers of images during the transmission line inspection, but most of them contain no critical components of transmission lines. Hence, it is a momentous task to adopt image classification algorithms to distinguish key images from all aerial images. In this work, we propose a novel classification method to remove redundant data and retain informative images. A novel transmission line scene dataset, namely TLS_dataset, is built to evaluate the classification performance of networks. Then, we propose a novel convolutional neural network (CNN), namely TL-Net, to classify transmission line scenes. In comparison to other typical deep learning networks, TL-Nets gain better classification accuracy and less memory consumption. The experimental results show that TL-Net101 gains 99.68% test accuracy on the TLS_dataset.

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“…In LSTM, a memory unit takes the place of each ordinary neuron in the hidden layer of standard RNN. The LSTM Memory block shown in Figure has an input gate, a forget gate and an output gate which regulate the flow of information in and out of the cell . The equations for these gates and cell states are presented as follows:

f_{t} = σ false(W_{f} \cdot false[h_{t - 1}, x_{t} false] + b_{f} false)

i_{t} = σ false(W_{i} \cdot false[h_{t - 1}, x_{t} false] + b_{i} false)

{\overset{C}{true}}_{t} = \tanh false(W_{c} \cdot false[h_{t - 1}, x_{t} false] + b_{c} false)

C_{t} = f_{t} * C_{t - 1} + i_{t} * {\overset{C}{true}}_{t}

o_{t} = σ false(W_{o} \cdot false[h_{t - 1}, x_{t} false] + b_{o} false)

h_{t} = o_{t} * \tanh C_{t},

where x t and h t are input and output vector at time

t, \overset{C}{true}

…”

Section: Deep Learning Based Missing Value Predictionmentioning

confidence: 99%

ANFIS and Deep Learning based missing sensor data prediction in IoT

Guzel

Kök

Akay

et al. 2019

Concurrency and Computation

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Summary Internet of Things (IoT) consists of billions of devices that generate big data which is characterized by the large volume, velocity, and heterogeneity. In the heterogeneous IoT ecosystem, it is not so surprising that these sensor‐generated data are considered to be noisy, uncertain, erroneous, and missing due to the lack of battery power, communication errors, and malfunctioning devices. This paper presents Deep Learning (DL) and Adaptive‐Network based Fuzzy Inference System (ANFIS) based prediction models for missing sensor data problem in IoT ecosystem. First, we build ANFIS based models and optimize their parameters. Then, we construct DL based models by using Long Short Term Memory (LSTM) network structure and optimize its parameters by applying the grid search method. Finally, we evaluate all the proposed models with Intel Berkeley Lab dataset. Experimental results demonstrate that the proposed models can significantly improve the prediction accuracy and may be promising for missing sensor data prediction.

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

Cited by 65 publications

References 26 publications

Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM

Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM

TL-Net: A Novel Network for Transmission Line Scenes Classification

ANFIS and Deep Learning based missing sensor data prediction in IoT

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