A Cyber-Physical Testbed for Hierarchical Fault Isolation in Bilateral Systems: Multi-Domain Fusion with Attention-Derived Laterality Attribution

Abstract

Prognostics and Health Management (PHM) for complex cyber-physical systems is constrained by a fundamental scarcity of labeled fault data, as catastrophic failures are operationally irreproducible and ethically impossible to induce at scale. To bridge this gap, this study utilizes a dual-engine aerospace platform as a high-fidelity case study, detailing a reproducible cyber-physical testbed leveraging the DCS World Su-25T simulation environment for hierarchical fault isolation. A robust real-time telemetry architecture extracts 25 sensor channels at a 30 Hz scheduler frequency via Apache Kafka and PostgreSQL, facilitating programmatic fault injection across a structured 3×3 altitude-speed grid (1,000 ft, 8,000 ft, and 20,000 ft altitude bands; 350, 400, and 450 kts TAS). The resulting dataset comprises 369 labeled missions spanning four distinct thermodynamic states: left-engine fire, right-engine fire, dual-engine fire, and a nominal baseline. Under a validated injection protocol with randomized onset (T+60s to T+80s), two research questions are addressed. RQ1 evaluates multi-domain sensor fusion through an XGBoost framework trained on a 789-feature vector encompassing time-domain statistics and cross-channel asymmetry indicators. RQ2 explores temporal fault laterality via a PyTorch Transformer encoder processing 450-timestep post-fault sequences. Model interpretability is achieved through SHAP domain contribution analysis and Attention-derived Domain Activation Profiles (DAPs). The XGBoost pipeline demonstrates exceptional diagnostic performance (Macro F1 = 0.9914), while the Transformer achieves Macro F1 = 0.9784 for laterality identification, maintaining robustness via leave-2-out cross-validation (mean F1 = 0.975). A novel methodological finding reveals that the Transformer identifies failure lateralization by attending to stable healthy-side channels, suggesting an information-theoretic entropy differential mechanism in bilateral systems.