Intelligent classification model for railway signal equipment fault based on SMOTE and ensemble learning

Yang, Lianbao; Li, Ping; Xue, Rui; Ma, Xiang; Li, Xinqin; Wang, Zhe

doi:10.1088/1757-899x/383/1/012042

Cited by 15 publications

(14 citation statements)

References 6 publications

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“…It was used to generate the fault data randomly for making the data balanced. Result showed that the sample has significantly improved the error classification [10].…”

Section: Related Workmentioning

confidence: 95%

Building a System based on Intelligent Agencies for Assisting in Classification and Circulation of Books and Periodicals Automatically

ElAlami¹,

Ahmed²,

Elemam³

2019

IJCA

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This paper presents an intelligent system to assist librarians in classification and circulation of books and periodicals. The system is based on a database containing the categories of scientific departments in the Faculty of Specific Education. It is also contains knowledge base that includes most of the words related to these categories. The Distance Vector Classifier Engine is used for text pre-processing classification. K-Nearest Neighbor algorithm and Term Frequency-Inverses Document Frequency are used for classification books and periodicals. The proposed system has achieved accuracy reached to 94.3%.

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“…It was used to generate the fault data randomly for making the data balanced. Result showed that the sample has significantly improved the error classification [10].…”

Section: Related Workmentioning

confidence: 95%

Building a System based on Intelligent Agencies for Assisting in Classification and Circulation of Books and Periodicals Automatically

ElAlami¹,

Ahmed²,

Elemam³

2019

IJCA

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“…In literature [11], for fault data generated by high-speed rail on-board equipment, a topic model is used to extract the feature information in the text, and the classification is completed by the Bayesian structure learning algorithm HDBN_SL. In literature [12], for unbalanced fault text data, the electrical service signal equipment fault text data was transformed into feature vectors by TF-IDF (Term Frequency-Inverse Document Frequency), and a voting-based approach was used to achieve integrated multiclassifier learning classification. Literature [13] used TF-IDF to transform on-board fault logs into feature vectors, built an ensemble classifier under the bagging framework, and used KELM (Kernel-Based Extreme Learning Machine) as the basic classifier to complete the text classification task; with the rise of deep learning in the field of NLP, it has also been applied to the field of text processing for rail transportation.…”

Section: Related Workmentioning

confidence: 99%

BERT-BiGRU Intelligent Classification of Metro On-Board Equipment Faults Based on Key Layer Fusion

Liu

Lin

2022

Wireless Communications and Mobile Computing

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Metro train operation may result in massive and complex unstructured fault text data. To solve the problem of low classification accuracy and incomplete classification effect of unstructured fault data automatic classification task, a BERT-BiGRU fault text classification model based on key layer fusion is proposed. Firstly, the unstructured text data is processed into word vectors with location information in the word embedding layer and then input into the BERT layer. Based on the traditional 12-layer BERT model, the semantic information is fully obtained by encoding the two-way transformer encoder in layers 2, 4, 6, 8, and 12 for fusion and dimensionality reduction, which is then input into the BiGRU layer to extract the context information to obtain the high-level feature representation of the text. After that, the final classification results are output in the output layer through the full connection layer FC and softmax functions. This model is tested with other mainstream models on the fault text data of metro on-board equipment. The experiment results show that on the same data set, the F 1 -score of this model is 7%~8% higher than that of the current mainstream classification model, that is, about 1~2% higher than that of other PLMs, and the F 1 -score of this model is higher than that of BERT model with different transformer layers, reaching the highest value of 0.9272, and the convergence speed in the training process is fast.

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“…First of all, the Chinese text content should be segmented. In this paper, the Jieba word segmentation tool based on the professional corpus and the common corpus is used to segment the signal fault text [13], and the auxiliary words such as "de," "Le," and other words that cannot represent the document features are cleaned up, and then, the TF-IDF is used to calculate the weight of the vocabulary set to form a vocabulary weight matrix, and the number of each vocabulary is counted to form a vocabulary dictionary. e TF-IDF weight matrix is the number of documents and the vocabulary of all documents, so it has a large dimension.…”

Section: High-speed Railway Signal Fault Textmentioning

confidence: 99%

Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining

Gao

Wang

et al. 2021

Journal of Electrical and Computer Engineering

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In this paper, the multilevel classification model of high-speed railway signal equipment fault based on text mining technology is proposed for the data of high-speed railway signal fault. An improved feature representation method of TF-IDF is proposed to extract the feature of fault text data of signal equipment. In the multilevel classification model, the single-layer classification model was designed based on stacking integrated learning idea; the recurrent neural network BiGRU and BiLSTM were used as primary learners, and the weight combination calculation method was designed for secondary learners, and k-fold cross verification was used to train the stacking model. The multitask cooperative voting decision tree was designed to correct the membership relationship of classification results of each layer. Ten years of signal switch machine fault data of high-speed railway are used for experimental analysis; the experiment shows that the multilevel classification model can effectively improve the classification of signal equipment fault multilevel classification task evaluation index and can ensure the correctness of the subordinate relations’ classification results.

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Intelligent classification model for railway signal equipment fault based on SMOTE and ensemble learning

Cited by 15 publications

References 6 publications

Building a System based on Intelligent Agencies for Assisting in Classification and Circulation of Books and Periodicals Automatically

Building a System based on Intelligent Agencies for Assisting in Classification and Circulation of Books and Periodicals Automatically

BERT-BiGRU Intelligent Classification of Metro On-Board Equipment Faults Based on Key Layer Fusion

Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining

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