Switch machine fault diagnosis based on cross-modal feature fusion and Transformer-XL with recursive memory mechanism

LIU Chang; YANG Shiwu; LIU Haiwei; BAI Yingjie; LIU Shanghe

doi:10.11860/j.issn.1673-0291.20250033

Abstract：The stable operation of switch machines is a key guarantee for the safety of High-Speed Rail-ways(HSRs).With the increasing demand for intelligent railway systems,higher requirements are im-posed on the precise perception and autonomous diagnosis of switch machine working conditions.To overcome the limitations of traditional methods in terms of diagnostic accuracy,computational real-time performance,and anti-interference capability,this paper constructs an intelligent diagnosis model that integrates cross-modal feature attention mechanism and Transformer-XL recursive memory mechanism.The proposed model enhances fault recognition and environmental adaptability under com-plex operating conditions.By introducing a cross-modal attention mechanism,it enables dynamic inter-action between power curve signals and switch rail vibration signals,mitigating the judgment bias caused by missing single-modal information.The Transformer-XL with recursive memory mechanism dynamically adjusts the model's perception of historical information,allowing it to extract state infor-mation across time windows.Additionally,1D-CNN is incorporated for short-term dynamic feature extraction,optimizing global sequential modeling while improving noise robustness and reducing com-putational complexity.Experimental results demonstrate that the proposed model exhibits significant advantages in cross-modal feature representation,long-term dependency modeling,noise robustness,and computational efficiency.This study provides an intelligent diagnostic solution for HSR mainte-nance,featuring high efficiency,low resource consumption,and strong environmental adaptability.It facilitates the transition from reactive maintenance to predictive maintenance,thereby improving opera-tional safety margins and robustness.