DT-FTA-ARM: A Collaborative Framework for Real-Time Fault Diagnosis in Subway Environmental Control Systems

Digital Twin (DT) introduces a novel paradigm of autonomous virtual-physical integration for fault diagnosis in Environmental Control Systems (ECS), with its core lying in the self-adaptive optimization of diagnostic capabilities through dynamic data mapping and logical evolution. Despite the progress made by existing DT-driven fault diagnosis methods in hybrid modeling, rule mining, and adaptive analysis, significant challenges persist. These challenges include the effective fusion of heterogeneous sensor data and the evolution of diagnostic logic under dynamic conditions. This paper systematically reviews core technical pathways for fault diagnosis, analyzes theoretical limitations based on current research, and proposes an innovative Digital Twin-Fault Tree Analysis-Association Rule Mining (DT-FTA-ARM) collaborative diagnostic framework. The proposed framework employs advanced algorithms to balance its computational efficiency with interpretability, addressing critical issues in multi-source data alignment and cross-system knowledge transfer. Field validation in subway ECS applications demonstrates the framework's potential to enhance operational reliability while adhering to strict latency and energy constraints.