A Platform View of Aircraft Data Simulation and Analysis

This paper introduces a platform-level approach for aircraft data simulation, offering a holistic perspective to study system interactions and fault propagation across flight phases. Building on the earlier work on Framework for Aerospace Vehicle Reasoning (FAVER), this work integrates altitude-dependent fault propagation and cascading effects through the use of a Virtual Aircraft Model (VAM). The Virtual Aircraft Model (VAM) is developed by integrating four aircraft system models into a unified model and automating the simulation of fault scenarios across Federal Aviation Administration (FAA)-defined flight phases. A solution vector is derived to efficiently manage extensive simulation runs, incorporating various systems, fault modes, flight phases, and degradation severities. Initial results validate the model’s effectiveness in capturing both healthy states and fault propagation cases in the data generated at both the system and platform levels. Given the huge amount of data generated, the Hyper-computing Integrated Layer for Digital Aviation (HILDA), a high-performance computing system, is used to ensure efficient simulation execution. Enhancements within the VAM, including an optimised "order of run," dictate the sequence of fault mode simulations to capture cross-system interactions. By enabling this platform-level approach through the VAM, the siloed system-specific analyses is avoided, bringing the potential of integrated feedback to original equipment manufacturers (OEMs) and maintenance personnel. This work establishes a foundation for future work on State of Health (SoH) and prognostic assessment, facilitating advanced health management.