ScholarWorks@동국대학교

초록

This study proposes an integrated framework to improve indoor air quality (IAQ) resilience and ventilation reliability in enclosed subway environments. Fault tree analysis and Monte Carlo simulations were used to identify critical vulnerabilities, supporting system-level fault prognostics. Three targeted strategies were developed: mechanical redundancy (MRS), health-aware feedback control (CRS), and data-driven fault detection and reconstruction (DRS). These strategies were experimentally and computationally validated, both individually and in combination. While standalone strategies offered partial improvements, the integrated application achieved the greatest enhancement, increasing the health-resilient ventilation index (HRVI) by 21.86% in normal and 23.31% in sensor fault conditions. The results highlight the necessity of multidimensional approaches that combine prognostics, health-aware control, and data assurance to ensure resilient IAQ management in complex subway systems. ISA

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