Research on remaining service life prediction of platform screen doors system based on genetic algorithm to optimise BP neural network

The platform screen door system related to the safety of passengers, vehicle and station operation in rail transit is the research object. Relay, the key component of the PSD system, is selected as the breakthrough point, and the number of pull-in is used as the evaluation index of the service life...

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Bibliographic Details
Published in:Enterprise information systems Vol. 16; no. 8-9
Main Authors: Ling, Xiang, Liu, Suping, Liu, Qin, Wei, Qianzhou, Zhang, Yu, Shi, Zihong
Format: Journal Article
Language:English
Published: Taylor & Francis 03.08.2022
Subjects:
ga-BP Neural Network
optimisation
Platform Screen Doors System
prediction
remaining Service Life
ISSN:1751-7575, 1751-7583
Online Access:Get full text
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Summary:The platform screen door system related to the safety of passengers, vehicle and station operation in rail transit is the research object. Relay, the key component of the PSD system, is selected as the breakthrough point, and the number of pull-in is used as the evaluation index of the service life of the relay. The precise and complex mapping between the influencing factors of the relay (coil resistance, pull-in voltage, release voltage, pull-in time, release time and contact resistance) and the remaining service life of the PSD system are established based on genetic algorithm to optimise BP neural network, and a new model for predicting the remaining life of the PSD is constructed. The PSD system of Guangzhou Metro Line 8 - KeCun Station is taken as the experimental test object. By comparing the BP algorithm, the PSO-BP algorithm and the GA-BP algorithm, it is demonstrated in detail how the prediction model can effectively predict the remaining service life of the PSD system with high precision, so that the equipment early warning before failure occurs to ensure the normal operation of subway lines. Experimental examples effectively prove the stability and accuracy of the GA-BP algorithm proposed.
ISSN:1751-7575
1751-7583
DOI:10.1080/17517575.2020.1864478