Rapid and precise location of the faults of on-board equipment of train control system is a significant factor to ensure reliable train operation.Text data of the fault tracking table of on-board equipment are taken a...Rapid and precise location of the faults of on-board equipment of train control system is a significant factor to ensure reliable train operation.Text data of the fault tracking table of on-board equipment are taken as samples,and an on-board equipment fault diagnosis model is designed based on the combination of convolutional neural network(CNN)and particle swarm optimization-support vector machines(PSO-SVM).Due to the characteristics of high dimensionality and sparseness of fault text data,CNN is used to achieve feature extraction.In order to decrease the influence of the imbalance of the fault sample data category on the classification accuracy,the PSO-SVM algorithm is introduced.The fully connected classification part of CNN is replaced by PSO-SVM,the extracted features are classified precisely,and the intelligent diagnosis of on-board equipment fault is implemented.According to the test analysis of the fault text data of on-board equipment recorded by a railway bureau and comparison with other models,the experimental results indicate that this model can obviously upgrade the evaluation indexes and can be used as an effective model for fault diagnosis for on-board equipment.展开更多
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 X...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-3train 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.展开更多
基金Gansu Province Higher Education Innovation Fund Project(No.2020B-104)“Innovation Star”Project for Outstanding Postgraduates of Gansu Province(No.2021CXZX-606)。
文摘Rapid and precise location of the faults of on-board equipment of train control system is a significant factor to ensure reliable train operation.Text data of the fault tracking table of on-board equipment are taken as samples,and an on-board equipment fault diagnosis model is designed based on the combination of convolutional neural network(CNN)and particle swarm optimization-support vector machines(PSO-SVM).Due to the characteristics of high dimensionality and sparseness of fault text data,CNN is used to achieve feature extraction.In order to decrease the influence of the imbalance of the fault sample data category on the classification accuracy,the PSO-SVM algorithm is introduced.The fully connected classification part of CNN is replaced by PSO-SVM,the extracted features are classified precisely,and the intelligent diagnosis of on-board equipment fault is implemented.According to the test analysis of the fault text data of on-board equipment recorded by a railway bureau and comparison with other models,the experimental results indicate that this model can obviously upgrade the evaluation indexes and can be used as an effective model for fault diagnosis for on-board equipment.
基金supported by the Science and Tec hnology Research and Development Plan Contract of China National Railway Group Co.,Ltd(Grant No.N2022G012)the Railway Science and Technology Research and Development Center Project(Project No.SYF2022SJ004).
文摘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-3train 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.
