Logo image
Back
Health prediction of metro platform screen door system based on XGBoost-BiGRU-attention model
Journal article   Open access   Peer reviewed

Health prediction of metro platform screen door system based on XGBoost-BiGRU-attention model

Zijun Zhu, Shaohu Tang, Liang Zhang, Hailin Kang, HongKang Song and Pengyu Li
Scientific reports
26 Jun 2026
DOI: https://doi.org/10.1038/s41598-026-59406-w
PMID: 42362812
url
https://doi.org/10.1038/s41598-026-59406-wView
Published, Version of Record (VoR) Open

Abstract

Attention mechanism Equipment health management EXtreme gradient boosting algorithm Bidirectional gated recurrent unit Metro platform screen door system Health prediction
To address the limitations of traditional metro equipment maintenance, such as heavy reliance on manual experience, poor mobility, difficulty assessing equipment health, and the inability to meet actual operation and maintenance needs, this paper proposes a health prediction method for metro platform screen door (PSD) system based on an XGBoost-BiGRU-Attention model optimized with the Lookahead optimizer. Using the PSD system's fault records and control principles, data were collected for 14 characteristic parameters. The XGBoost algorithm was applied to rank these variables in order of importance, and the top-ranking characteristic parameters were selected as inputs to the prediction model. An attention mechanism was then integrated into a Bidirectional Gated Recurrent Unit (BiGRU) network using the Lookahead optimizer to improve prediction performance. Results show that, compared with a standard BiGRU model, the proposed XGBoost-BiGRU-Attention model reduces Root Mean Square Error (RMSE) by 52.4%, Mean Absolute Error (MAE) by 57.1%, and Mean Absolute Percentage Error (MAPE) by 57.0%, achieving a coefficient of determination of 0.972. These findings indicate that the proposed model offers strong practicality and feasibility for health management of metro equipment.

Metrics

1 Record Views

Details

Logo image