Civil aviation is currently facing an unprecedented, multi-billion-dollar operational crisis centered on the Pratt & Whitney Geared Turbofan (GTF) engine family. Between 2015 and 2021, microscopic ceramic tooling particles contaminated the powdered metal used to forge high-pressure turbine and compressor discs. Under extreme operational stress, these impurities cause subsurface micro-cracks that risk catastrophic, uncontained engine failures.

To mitigate this, regulatory authorities issue complex, 60-page Airworthiness Directives (ADs) and Service Bulletins (SBs) mandating aggressive inspection thresholds. Because these discs are buried deep within the engine core, verification requires total engine disassembly, ballooning turnaround times to 250–300+ days per engine.

The primary operational bottleneck is a critical data silo: airline maintenance coordinators must manually parse unstructured, legalistic PDFs to map thousands of affected serial numbers against their internal Digital Twin databases (e.g., AMOS or TRAX). This manual cross-referencing introduces significant human error risks—where a single missed sub-clause can lead to catastrophic failure or regulatory fines—and leaves fleet managers operationally blind to immediate grounding timelines ("drop-dead dates").

The Solution: AI-Driven Airworthiness Triage Engine

The proposed solution is an automated, localized software application acting as a tactical "first responder" for maintenance command centers. The workflow operates in three distinct phases:

• Intelligent Ingestion: Users drop unstructured regulatory PDFs into a completely offline, local environment.

• Deterministic Constraint Extraction: Utilizing a localized, open-source Large Language Model, the system strips away legal prose to cleanly isolate four core variables: Part Numbers, Serial Number ranges, Flight Cycle Limits, and Environmental Triggers.

• Active Fleet Synchronization: The engine structures these metrics to query the airline's active fleet database.

Key architectural innovations include a Zero-Cloud Local Parser to satisfy strict aviation cybersecurity requirements, a "Drop-Dead" Predictor that cross-references live engine telemetry to pinpoint exact operational deadlines, and a Dynamic Slot Queue Optimizer to prioritize maintenance schedules and minimize flight disruptions. Rather than performing a basic text search, this engine treats text as executable code, reducing manual document processing times by over 90%.

Technical Architecture

Frontend & Dashboard: Built with Streamlit to provide an intuitive drag-and-drop web interface and live operational dashboards.

AI Core: Meta Llama 3 via Ollama, allowing a compact LLM to parse technical text locally without cloud leakage.

Document Processing: LangChain / LlamaIndex to chunk dense manuals so the model isolates parts numbers efficiently.

Digital Twin Database: SQLite / PostgreSQL to store active aircraft manifests, component histories, and flight hours.

Data Integration: Python & Text-to-SQL to automatically transform extracted rules into database queries.

Key Benefits & Impact

95% Faster Processing: Compresses days of manual spreadsheet auditing into under 10 seconds.

Proactive Grounding Prevention: Projects precise maintenance windows months in advance, replacing reactive panic with structured logistical scheduling.

Elimination of Human Error: Automates exact serial number matching, mitigating the catastrophic risks and financial penalties of un-airworthy operations.