Work

Robust Disturbance-Response Feature Modeling and Multi-Perspective Validation of Compensation Capacitor Signals

Year: 2026

Type: article

Abstract: In high-speed railways, the reliability of jointless track circuits largely hinges on the operational integrity of compensation capacitors. These capacitors are periodically installed along the track to mitigate rail inductive impedance and stabilize signal transmission. The induced voltage response, referred to as the compensation-capacitor signal, serves as a critical diagnostic indicator of circuit health. Yet it is often distorted by electromagnetic interference and structural resonance, posing significant challenges for robust feature extraction. To address this challenge, we propose a Disturbance-Robust Feature Distillation (DRFD) framework that performs multi-perspective modeling and validation of robust features. The framework formulates a unified multi-objective optimization model that jointly considers statistical significance, environmental stability, and structural separability. These objectives are harmonized through an adaptive Bayesian weighting mechanism, enabling automatic identification of disturbance-resistant and discriminative features under complex operating conditions. Experimental evaluations on real-world datasets collected at a 100 kHz sampling rate from roadbed, tunnel, and bridge environments demonstrate that the DRFD framework achieves 96.2% accuracy and 95.4% F1-score, outperforming the best-performing baseline by 4.2–7.8% in accuracy and 6.5% in F1-score. Moreover, the framework achieves the lowest cross-condition relative variance (RV < 0.015), confirming its high robustness against electromagnetic and structural disturbances. The extracted core features—Root Mean Square (RMS), Peak Factor (PF), and Center Frequency (CF)—faithfully capture the intrinsic electromagnetic behaviors of compensation capacitors, thus linking statistical robustness with physical interpretability for enhanced reliability assessment of railway signal systems.

Source: Mathematics

DOI: 10.3390/math14020316

Authors: Tongdian Wang, Pan Wang

Institution: Nanjing University of Posts and Telecommunications

Language: English

Topic: Transportation Systems and Safety

Subfield: Computer Graphics and Computer-Aided Design

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137