A state monitoring method, system and related device of a suspension control system

Abstract

The present application provides a state monitoring method for a suspension control system, which includes: acquiring input data and output data of the suspension control system; determining the corresponding model numerator and denominator order and the closed-loop transfer function of the suspension control system according to the input data; according to the closed-loop transfer function Calculate the open-loop transfer function; determine the transfer function of the controlled object according to the open-loop transfer function and the initial controller transfer function; determine the gap metric according to the transfer function of the controlled object and the first preset formula; determine the suspension according to the interval corresponding to the gap metric Control the health of the system. The present application uses the gap measurement between the transfer function of the controlled object and the original system's controlled object transfer function as the health index of the system, so as to realize health monitoring and avoid train failure, serious economic losses and high maintenance costs. The present application also provides a state monitoring system, a computer-readable storage medium and an electronic device of a suspension control system, which have the above beneficial effects.

Description

technical field [0001] The present application relates to the field of electronic equipment safety, in particular to a state monitoring method, system and related devices of a suspension control system. Background technique [0002] The levitation system is one of the core systems of the maglev train. If the suspension system breaks down, the train will not be able to run, and serious economic losses and maintenance costs will result. System failure is not that the system is completely inoperable, but that the performance of the system is not within the ideal range. [0003] In order to solve this problem, fault prediction and health management are required. At present, the vast majority of fault prediction and health management are abnormal detection, health monitoring, fault diagnosis, fault prediction and health management of the system, among which the health monitoring technology mainly adopts the deterministic index and robustness index in the performance index of th...

AI Technical Summary

Problems solved by technology

[0004] Most of the existing fault prediction and health management technologies only monitor and predict the state of the system, and do not take corresponding measures to prolong the remaining service life of the system in the process of system performance degradation

Secondly, the control strategy of extending the remaining service life of the system by changing the control parameters needs to repeatedly change the controller parameters and download the program to the control board, or even find better controller parameters, which will result in a lot of work and effort. time, which directly leads to poor monitoring and poor performance

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