Research on text fault recognition for on-board equipment of a C3 train control system based on an integrated XGBoost algorithm

The robust guarantee of train control on-board equipment is inextricably linked to the safe functioning of a high-speed train. A fault diagnostic model of on-board equipment is built utilizing the integrated learning XGBoost (eXtreme Gradient Boosting) algorithm to help technicians assess the malfun...

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Vydané v:Transportation safety and environment Online Ročník 5; číslo 4
Hlavní autori: Yue, Lili, Liu, Luyue, Li, Maoqing, Xiao, Baodi, Wu, Xiaochun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Changsha Oxford University Press 01.09.2023
Predmet:
Accuracy
Adaptive sampling
Algorithms
Control systems
Decision trees
Fault diagnosis
Fault location
High speed rail
Locomotives
Monitoring systems
Onboard equipment
Robust control
Technicians
Test sets
Training
text feature extraction
unbalanced data sets
fault diagnosis
vehicle on-board equipment
XGBoost model
ISSN:2631-4428, 2631-4428
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Shrnutí:The robust guarantee of train control on-board equipment is inextricably linked to the safe functioning of a high-speed train. A fault diagnostic model of on-board equipment is built utilizing the integrated learning XGBoost (eXtreme Gradient Boosting) algorithm to help technicians assess the malfunction category of high-speed train control on-board equipment accurately and rapidly. The XGBoost algorithm iterates multiple decision tree models to improve the accuracy of fault diagnosis by lifting the predicted residual and adding regular terms. To begin, the text features were extracted using the improved TF-IDF (Term Frequency–Inverse Document Frequency) approach, and 24 fault feature words were chosen and converted into weight word vectors. Secondly, considering the imbalanced fault categories in the data set, the ADASYN (Adaptive Synthetic sampling) adaptive synthetically oversampling technique was used to synthesize a few category fault samples. Finally, the data samples were split into training and test sets based on the fault text data of CTCS-3 train control on-board equipment recorded by Guangzhou Railway Group maintenance personnel. The XGBoost model was utilized to realize the automatic fault location of the test set after optimized parameter tuning through grid search. Compared with other methods, the evaluation index of the XGBoost model was significantly improved. The diagnostic accuracy reached 95.43%, which verifies the effectiveness of the method in text fault diagnosis.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2631-4428
2631-4428
DOI:10.1093/tse/tdac066