Auxiliary system self-inspection system and method suitable for unmanned train

Abstract

The invention relates to an auxiliary system self-inspection system and method suitable for unmanned trains. The method includes: establishing a fault knowledge base for each auxiliary subsystem to be tested; when the network communication of the train and the communication of each auxiliary subsystem to be tested are normal , each auxiliary subsystem to be tested enters the low-voltage self-inspection mode; after the low-voltage self-inspection is normal, each auxiliary subsystem to be tested enters the high-voltage self-inspection mode; the results of the high-low voltage self-inspection and the matching fault suggestion solution in the fault knowledge base are fed back To the signal vehicle controller, send to the central signal system. The invention integrates functionally related auxiliary subsystems for patterned detection, uses generalized auxiliary systems to perform static self-inspection of trains, and obtains corresponding faults by establishing the fault knowledge base of each auxiliary subsystem and combining their own high and low voltage self-inspection results The matching fault suggestion solution in the knowledge base does not require additional hardware, which is convenient for operation dispatchers to coordinate and process, and improves fault location accuracy and detection efficiency.

Description

technical field [0001] The invention belongs to the technical field of unmanned train driving, and in particular relates to an auxiliary system self-inspection system and method suitable for unmanned trains. Background technique [0002] Like conventional subways, unmanned trains also need to check the vehicle status before going online, but conventional subways rely on manual inspections of trains to complete daily inspections, and complete power-on and train inspections before going online. Existing unmanned trains can Relying on the self-inspection of each subsystem to wake up and detect the train remotely, the time-consuming has been reduced to a certain extent, but it is still necessary to manually issue inspection commands to each subsystem, and analyze and judge the returned self-inspection results to confirm whether each system of the vehicle meets the requirements of operation. It is required to judge how to deal with the problems that arise, and the dispatchers sti...

AI Technical Summary

Problems solved by technology

[0002] Like conventional subways, unmanned trains also need to check the vehicle status before going online, but conventional subways rely on manual inspections of trains to complete daily inspections, and complete power-on and train inspections before going online. Existing unmanned trains can Relying on the self-inspection of each subsystem to wake up and detect the train remotely, the time-consuming has been reduced to a certain extent, but it is still necessary to manually issue inspection commands to each subsystem, and analyze and judge the returned self-inspection results to confirm whether each system of the vehicle meets the requirements of operation. Requirements, judging how to deal with the problems that arise, still need to participate in the dispatching personnel to have a high degree of professionalism, and only the functionality of the system has been tested and the impending failure cannot be found

[0003] For example, the patent CN110949450A discloses a self-inspection method for the orthogonal traction system, which can detect in advance whether each device in the converter and each functional circuit is faulty before the train starts to operate. Waiting for testing, and need to send commands to disconnect each contactor in a loop, which takes a long time and requires manual measures to deal with the test results, without system-level interaction, it is not suitable for intelligent unmanned train applications

[0004] To sum up, in the rail transit application of unmanned driving solutions, with the rapid development and application of intelligent operation and maintenance systems, the existing system self-inspection methods have certain limitations, and cannot be applied in intelligent unmanned driving application scenarios

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