Health Prognostics of Rail Balise Communications With Large Language Model via Closed-Loop Perception-Adaptation-Feedback Reasoning

Balise health prognostics is critical to proactively maintaining the reliable operation of high-speed railway wireless communication. However, the intricately evolving nature of balise health under coupled ground-train transmission dynamics across rail lines challenges generalized and accurate prognostics. To tackle it, this article proposes an explicit-implicit chain-of-thought (CoT) framework that endows a large pretrained language model (PLM) with desired prognostic power via closed-loop reasoning comprising perception, adaptation, and feedback. Specifically, a stepwise instruction dispatcher is introduced to synthesize balise measurement data with situational context and logic rules, tailoring conditional directives for varying line scenarios. It can prompt PLM to progressively perceive distinctive health evolution signatures and deterioration cues throughout multistep reasoning, shaping an explicit, causally traceable CoT for generalized prognostics. A dual-granularity adaptor is built to persistently capture coupling relationships among health deterioration factors and sequentially append their unique nuances, forming an implicit, highly adaptable CoT for PLM to improve prognostic accuracy. A regulatory routing mechanism is devised to harmonize explicit and implicit CoTs, while optimizing their collaboration via iterative feedback, mitigating drift of thought to boost prognostic performance. Experiments on hundreds of real-world balises across multiple lines demonstrate that the proposed method achieves accurate health prognostics with strong causal interpretability.