It is of great significance to extract the features of a large number of fault text data generated during the operation of train control vehicle equipment in the subsequent fault diagnosis task. In this paper, a fault diagnosis model based on BiGRU and CNN based on dual-channel feature fusion is constructed. Firstly, the dynamic word vectors were generated by the BERT pre-trained model from the data cleaning fault vocabulary database. Then, in order to fully extract the global and local semantic information of the data, a fault diagnosis model based on BiGRU-CNN dual-channel feature fusion was constructed, and an improved cross-entropy loss function was introduced to focus on the difficult to classify samples. Finally, the fused high-dimensional feature vector matrix is reduced by the PCA method, the redundant information is removed, and the SVM classifier is used to complete the fault data classification and realize fault diagnosis. Experimental results show that the model can obtain word vectors with strong representation ability, and the extracted features with this word vector as the input of the dual-channel model have global and local semantic information, and can effectively solve the problem of inaccurate classification of small sample data. The model in this paper has significantly improved the accuracy, recall rate, and F1 value, which can provide some support for the daily diagnosis of train control vehicle equipment.
KEYWORDS: Education and training, Control systems, Systems modeling, Modeling, Telecommunications, Motion analysis, Safety, Instrument modeling, General packet radio service, Computer simulations
CTCS-3 train control system is the key system to ensure the safe operation of trains, and its effective combination with automatic train operation technology, which plays an important role in the control of train operation, and is also the mainstream direction of the development of high-speed railway system in China. A method in this paper is provided for modeling and formal simulation verification of the real-time behavior of the system based on timed automata to ensure the safe operation of high-speed railway C3+ATO system. Taking the function of movement authority generation as an example, according to the functional requirements of the train operation system, the information is transmitted between the communication equipment, establishes the timed automata network model of information interaction between the equipment, and the message sequence charts between the communication equipment is generated, which used in the function of movement authority generation, train sends unconditional emergency stop messsage and temporary speed reduction messsage through the formal simulation, and the security, existence and reachability of the system are verified by the BNF (Backus-Naur Form, BNF) statement. As a result, the model meets the requirements of system functional attributes and the technical specification of Radio Block Center, which lays a foundation for the subsequent research of C3+ATO system.
With the rapid development of the production economy, electrified EMU trains have become the main means of transportation for people to travel. But the ensuing power quality problem has also become one of the main concerns in the railway field today. When the electric locomotive is running, the harmonic currents in the traction current may cause interference to the railway signaling equipment (track circuits) along the line. Therefore, in this paper, we first simulate and analyze the traction current of the locomotive, and then take the useful signal 2600Hz~2630Hz, the harmonic signal 2550Hz, and 2650Hz as an example, and design a FIR band-pass filter based on genetic annealing algorithm. Compared with the traditional digital filter, it can be intuitively observed that the optimized filter filters out harmonic interference efficiently.
The research focus of this paper is the on-board equipment of the CTCS 300T train operation control system, and a fault diagnosis method for train control on-board equipment based on Bayesian network is proposed. Firstly, to address the issue of imbalanced distribution of fault types in fault text, we have developed a Three-way Oversampling (3WOS) algorithm to automatically generate subclass text vector data. To tackle the problem of multiple synonyms and single semantics in fault text, we utilize Supervised Latent Dirichlet Allocation (SLDA) to conduct semantic clustering and feature analysis on the fault tracking table, and combine expert knowledge to establish a comprehensive fault information database. Then, we employ the K2 algorithm to train and integrate the collected fault information for building a Bayesian network. Finally, diagnostic reasoning is conducted using actual cases from high-speed railway operation sites of railway bureaus, and experimental results validate that our model exhibits high accuracy and feasibility.
KEYWORDS: Reliability, Systems modeling, Safety, Control systems, Acquisition tracking and pointing, Complex systems, Quantitative analysis, Data modeling, Statistical analysis, General packet radio service
The High-speed railway automatic operation system plays an important role in controlling train operation and its function is related to the safety of automatic train operation. As an important part of the on-board subsystem to ensure the safety of train operation, it is necessary to model and analyze its reliability and safety. In view of the complex calculations of traditional reliability analysis methods and the difficulty of analyzing common cause failures, this paper analyzes the functional structure of the on-board subsystem of the High-speed railway automatic train operation system. Through the mapping relationship between the fault tree and Bayesian network, the Bayesian network model of the on-board subsystem is established, and the reliability of the on-board subsystem of High-speed railway automatic train operation system is modeled and analyzed. In this paper, the α factor model is used to quantitatively analyze the common cause failures of the on-board subsystems, and then the reliability analysis model of the on-board subsystems considering common cause failures is established by adding common cause failure nodes. The results show that the two-way inference ability of Bayesian networks can be used to analyze the availability and weaknesses of on-board subsystems. Through the continuous accumulation of common cause failure data of on-board subsystem, the quantitative analysis results of the α factor model are more in line with the actual failure rate.
To effectively detect the surface cracks of subway tunnels, an automatic tunnel crack detection system based on machine vision is presented. Aiming at the problems of environmental complexity and low contrast in subway tunnels, the image texture feature is first enhanced by the methods of frequency domain filtering and spatial differencing. Then, depending on the characteristics of the tunnel cracks in question, the crack propagation method is used to extract the complete cracks. Finally, broken cracks are connected during processing, and the method of combining projection and threshold is used to determine the crack types. At the same time, characteristics such as the length, width, and area of the cracks are obtained. The experimental results show that the presented methods can effectively extract complete cracks in complex tunnel environments. The identification error of tunnel crack parameters meets the actual engineering requirements.
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