Comparison of Feature and Information Fusion Health Indexes in Railway Balise Health Assessment

With the development of dynamic detection technology, the amount of data related to railway ground equipment has grown significantly. To make full use of this data, researchers have proposed various health state assessment techniques that are crucial for intelligent maintenance and health management. This paper introduces a novel balise health state assessment method that integrates multiple feature information to evaluate the health status of condition features that lack standard test criteria. Additionally, a differential information fusion method based on Dempster-Shafer (D-S) evidence theory is applied to combine different health indices. The proposed method is validated through comparative analyses of single and multiple degradation scenarios of key components, demonstrating its effectiveness and applicability in diverse scenarios for comprehensive health assessment.