Bi-TLLDA and CSSVM based fault diagnosis of vehicle on-board equipment for high speed railway

Abstract

Vehicle on-board equipment (VOBE) is a significant component of the control system of high-speed railway train, the fault diagnosis of VOBE mainly depends on maintenance experience, which is inefficiency. The fault data of on-board equipment is described by natural language. Due to its unstructured, high-dimensional and unbalanced fault class distribution, it has become a challenge in fault diagnosis. In this paper, bilevel topic labeled latent Dirichlet allocation for extraction feature of fault text data is proposed. Firstly, label information is set according to prior knowledge of railway field. Then, local topics and global topics are defined for two level fault types of VOBE. The fault feature space generated by Gibbs sampling from local topics and global topics contains two level fault features, which are conducive to fault text classification. Finally, considering the imbalanced distribution of fault class, cost sensitive support vector machine based fault text classification is proposed. By using actual fault data of on-board equipment of China Railway Corporation, the accuracy, precision and F1-score are used as performance indicators to compare the proposed method with other fault diagnosis methods. The results show that the accuracy of the proposed method is 90.3%, which is about 2% higher than that of the suboptimal method, and the average recall, precision and F1-score of various fault classes are 77.9%, 91.8% and 83.4%, respectively, which outperforms other fault diagnosis methods.

Fault Diagnosis Method for Railway Signal Equipment Based on Data Enhancement and an Improved Attention Mechanism

“…Shang [19] utilized a labeled-LDA probabilistic topic model to extract the fault text data characteristics of vehicle equipment within the train control system. Wei [20] incorporated prior knowledge in the railways field to calibrate label information, employed a cost-sensitive support vector machine to address class imbalances in fault data, and subsequently applied the latent Dirichlet allocation method with local and global double-layer topic labels for feature extraction in fault text classification. Song [3] utilized the Word2Vec model for processing fault terms and generating word vectors, which were then used to extract the fault text features of train control vehicles through a CNN.…”

“…Similarly, in the field of railway signal equipment fault diagnosis, researchers have also conducted relevant research and exploration. Wei [5] employed word frequency weighting to enhance the word vectors generated by the BERT model for extracting text feature vectors. Subsequently, a combination of BiLSTM and an improved attention mechanism was utilized to classify the fault text of train control vehicle equipment and enable fault diagnosis.…”

“…At present, the task of fault classification for signal equipment is still completed by equipment maintenance personnel, and the classification results may be inaccurate and arbitrary. Driven by the current development direction of railway big data and intelligent operations and maintenance, research on fault diagnosis models based on text data can mine the pattern relationships between fault records and corresponding fault equipment categories, achieve automatic classification and processing of fault data, and provide efficient theoretical reference for maintenance personnel to quickly locate and address faults according to fault phenomena when equipment fails [3][4][5].…”

A Text-Driven Aircraft Fault Diagnosis Model Based on Word2vec and Stacking Ensemble Learning

“…et al [29] proposed an automatic fault detection model that compares images of an aircraft with images taken of a properly functioning, identical aircraft to conclude whether sections of the aircraft are faulty and need maintenance. Wei et al [30] proposed bi-level, topic-labeled latent Dirichlet allocation for extracting features of text data and a cost-sensitive support vector machine (CSSVM)-based fault text classification method. Wang et al [31] proposed a bi-level (at syntax and semantic) feature extraction-based text mining technique for fault diagnosis to meet the challenges of high-dimensional data and imbalanced fault class distribution.…”