The “Last Mile” of PHM:: A Verifiable Claim Architecture for Deterministic Airworthiness Compliance Assessment

Abstract

To accelerate the exhaustive process of military airworthiness certification, this paper introduces a deterministic, Natural Language Processing driven workflow that establishes a Verifiable Claim Architecture (VCA). While PHM strategies successfully detect anomalies and predict subsystem failures, mapping these low-level engineering findings to high-level regulatory mandates remains a massive documentation bottleneck. The proposed VCA automates evidence discovery by integrating multi-format layout parsing and reproducible Boolean filtering to establish a highly structured, auditable evidence layer. Large Language Models are then deployed strictly as constrained text-synthesis engines, operating within isolated context windows to populate formal compliance matrices.

To fundamentally eliminate textual hallucination and abstractive drift, a provenance audit layer anchors every generated narrative block to an unalterable source document metadata tag. Furthermore, the architecture integrates system safety ontologies to automatically map engineering deviations to standardized Risk Assessment Codes (RAC) and operational flight restrictions in accordance with MIL-STD-882E. Evaluated by the Air Force Life Cycle Management Center against 7,346 pages of compliance artifacts across multiple aircraft platforms, the framework demonstrated a highly conservative safety posture, properly deferring policy exceptions to human engineers. By reducing expert administrative labor hours by an estimated 50% and compressing certification schedules from 18–36 months down to 6–12 months, this domain-agnostic framework effectively streamlines the compliance lifecycle, ensuring human experts remain focused entirely on authoritative, high-consequence risk assessment.