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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