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On Fault Analysis Method for CTCS On-Board Equipment of Railway Based on Hidden Markov Model

Published: 01 June 2024 Publication History

Abstract

Abstract: The on-board equipment of train control system will generate corresponding log files during operation, which contain a large amount of equipment operation information, including the record of on-board equipment fault information. It is of great significance to efficiently and accurately extract key fault information from log files for equipment fault identification and analysis during train operation. To solve the above problems, this paper proposes a text information extraction method based on hidden Markov model. Firstly, combining the content of the log file generated by the computer of the on-board equipment security and the fault information records of professionals, the information mining of the log file is carried out to construct the on-board equipment fault word database. On this basis, according to the characteristics of log files, the automatic preprocessing of log files and staff text records is realized. The pre-processed log files are marked to establish the initial character dictionary, and then the parameters of the model are reevaluated to obtain the trained model files. Then the test samples are marked and extracted according to the trained model files, so as toH realize the extraction of fault information in the log files. The Matlab simulation platform is used to build a fault analysis tool, so as to improve the processing efficiency of on-board equipment faults and increase the accuracy of fault discovery.
Keywords: On-board equipment; Failure analysis; Hidden Markov Chain; Log file

References

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CVDL '24: Proceedings of the International Conference on Computer Vision and Deep Learning
January 2024
506 pages
ISBN:9798400718199
DOI:10.1145/3653804
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Published: 01 June 2024

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