2015 IEEE 18th International Conference on Intelligent Transportation Systems 2015
DOI: 10.1109/itsc.2015.138
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Prior LDA and SVM Based Fault Diagnosis of Vehicle On-board Equipment for High Speed Railway

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Cited by 8 publications
(5 citation statements)
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“…Thus for Automatic understanding of domain specific texts and analyze railway accident narratives, deep learning has been conducted, which bestowed an accurately classify accident causes, notice important differences in accident reporting and beneficial to safety engineers [55].Also text mining conducted to diagnose and predict failures of switches [56]. For high-speed railways, fault diagnosis of vehicle onboard equipment, the prior LDA model was introduced for fault feature extraction [57] and for fault feature extraction the Bayesian network (BN) is also used [58]. For automatic classification of passenger complaints text and eigenvalue extraction, the term frequency-inverse document frequency algorithm been used with Naive Bayesian classifier [59].…”
Section: Related Workmentioning
confidence: 99%
“…Thus for Automatic understanding of domain specific texts and analyze railway accident narratives, deep learning has been conducted, which bestowed an accurately classify accident causes, notice important differences in accident reporting and beneficial to safety engineers [55].Also text mining conducted to diagnose and predict failures of switches [56]. For high-speed railways, fault diagnosis of vehicle onboard equipment, the prior LDA model was introduced for fault feature extraction [57] and for fault feature extraction the Bayesian network (BN) is also used [58]. For automatic classification of passenger complaints text and eigenvalue extraction, the term frequency-inverse document frequency algorithm been used with Naive Bayesian classifier [59].…”
Section: Related Workmentioning
confidence: 99%
“…For different varied scenes, researchers may use varied feature extraction methods, which may be related to the different text features in each real scene: the fault text records in some scenes are better represented by keywords, whereas some scenes rely more on semantic analysis. Researchers usually choose appropriate methods for modeling by comparing the accuracy of different text feature extraction methods and machine learning methods [11,14,15]. Second, according to the extracted text features, they build an appropriate model for fault diagnosis, prediction, or other analysis.…”
Section: Text Feature Extractionmentioning
confidence: 99%
“…The selection of a machine learning algorithm is the same as that for structured data, which need to be selected according to the characteristics of the research object. Wang et al [15] used a priori LDA model to extract the features of high-speed railway fault text records and established a fault location model based on SVM. By comparing the accuracy of classification models, it was found that the potential LDA model was better than the TF-IDF and traditional LDA models used in fault text feature extraction for high-speed railways.…”
Section: Text Feature Extractionmentioning
confidence: 99%
“…The applicability of case-based and gray correlation fault diagnosis methods in train control system has also been shown by some studies. 97,98 In the area of track circuit fault diagnosis, intelligent fault diagnosis method has developed quite rapidly. Zhao 99 took the key equipment track circuit as the research object.…”
Section: Case Study About Fault Diagnosis Of the High-speed Train Conmentioning
confidence: 99%