Research on Enterprise Hidden Danger Association Rules Based on Text Analysis

Ge, Shengxin; Zhuang, Yufeng; Hu, Yanzhu; Ai, Xinbo

doi:10.1088/1755-1315/252/3/032170

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…After extracting text features, Zhu [25] used BiLSTM with Support Vector Machine (SVM) to achieve a good classification result. In addition, Naive Bayesian (NB) algorithms [26] combined with association rules [27] have been widely used in the rail fault diagnosis domain. For example, Xie [28] used an NB classifier to achieve urban rail ground device fault classification with a fault log.…”

Section: Fault Diagnosis Of Rail Transit With Text Datamentioning

confidence: 99%

Fault Diagnosis of a Switch Machine to Prevent High-Speed Railway Accidents Combining Bi-Directional Long Short-Term Memory with the Multiple Learning Classification Based on Associations Model

Lin,

Hu,

et al. 2023

Machines

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The fault diagnosis of a switch machine is vital for high-speed railway operations because switch machines play an important role in the safe operation of high-speed railways, which often have faults because of their complicated working conditions. To improve the accuracy of turnout fault diagnosis for high-speed railways and prevent accidents from occurring, a combination of bi-directional long short-term memory (BiLSTM) with the multiple learning classification based on associations (MLCBA) model using the operation and maintenance text data of switch machines is proposed in this research. Due to the small probability of faults for a switch machine, it is difficult to form a diagnosis with the small amount of sample data, and more fault text features can be extracted with feedforward in a BiLSTM model. Then, the high-quality rules of the text data can be acquired by replacing the SoftMax classification with MLCBA in the output of the BiLSTM model. In this way, the identification of switch machine faults in a high-speed railway can be realized, and the experimental results show that the Accuracy and Recall of the fault diagnosis can reach 95.66% and 96.29%, respectively, as shown in the analysis of the ZYJ7 turnout fault text data of a Chinese railway bureau from five recent years. Therefore, the combined BiLSTM and MLCBA model can not only realize the accurate diagnosis of small-probability turnout faults but can also prevent high-speed railway accidents from occurring and ensure the safe operation of high-speed railways.

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Section: Fault Diagnosis Of Rail Transit With Text Datamentioning

confidence: 99%

Fault Diagnosis of a Switch Machine to Prevent High-Speed Railway Accidents Combining Bi-Directional Long Short-Term Memory with the Multiple Learning Classification Based on Associations Model

Lin,

Hu,

et al. 2023

Machines

View full text Add to dashboard Cite

show abstract

“…In literature [3], vehicle log is converted into vector form through vector space model, and rough set is used to select features on vectorization data. In text classifiers, traditional machine learning classification models such as Naive Bayes (NB) [4], Support Vector Machines (SVM) [5], and Association Rules [6] have achieved good results, such as using SVM models in reference [7] to classify railway turnout fault texts; Reference [8] applies association rules to fault classification of railway signal equipment; The Convolutional Neural Networks (CNN) model proposed by Kim [9] extracts text features through multiple convolutional kernels, and then uses the fully connected neural network inherent in the model for classification. CNN can deeply mine text features, thereby enhancing the expression ability of the original input data and achieving better classification results.…”

Section: Introductionmentioning

confidence: 99%