> Initializing Aerospace Research Data... [DONE]
A three-layer, edge-deployable predictive maintenance framework for the Boeing 787-8 main landing gear. Layer 1 deploys an ODE synthesis engine generating 18,000 physics-valid fault profiles in 7.9 minutes at 300× the speed of Simscape Multibody. Layer 2 uses a zero-leakage CNN-BiLSTM with Temporal Self-Attention achieving zero false negatives on catastrophic faults. Layer 3 integrates an offline Ollama 3.2 LLM translating neural outputs into ATA 32-compliant directives in under 1.5 seconds — fully air-gapped.
A multi‑protocol security framework integrating a permissioned blockchain (Hyperledger Fabric) with a four‑layer Physics‑Informed Digital Twin (PIDT) to detect both cyber‑attacks and physically impossible data injections. Every packet signed with Ed25519, Merkle‑batched, and anchored on-chain with a semantic integrity verdict.
A closed‑loop PHM Digital Twin for the Boeing 787‑8 ignition system (ATA 74). Overcomes run‑to‑failure data scarcity with a physics‑informed MATLAB ODE solver generating 27,000 samples across 11 fault modes. A LightGBM regressor (R² 0.87, MAE 39 cycles) maps micro‑electrical degradation to RUL, deployed via a decoupled FastAPI microservice with "Deep Feature Spoofing" God-Mode GUI.
A certification-compliant Cyber-Physical System fusing MATLAB Simscape Multibody physics with a dual-input LSTM deep learning network via PostgreSQL. The confidence-weighted fusion architecture explicitly satisfies CS-23.473 hard-landing regulations — triggering mandatory ATA 32-00 inspections at sink rate ≥ 3.05 m/s or acceleration ≥ 1.8g. Trained on 9,500 Dornier 228 landing cycles across 11 ATA-32 fault classes.
Revolutionizing computational fluid dynamics by replacing traditional Navier-Stokes solvers with Physics-Informed Neural Operators (PINNs). The interactive engine below uses Aerosandbox/XFoil for real-time demonstration; final deployment will transition to ANSYS Fluent for industrial-grade predictions.
A real-time, IoT-driven pipeline integrity monitoring platform designed for the Nigerian National Petroleum Corporation (NNPC) crude oil network. The system fuses distributed fibre-optic and MEMS pressure sensor arrays with an LSTM-Autoencoder anomaly engine to detect leaks, corrosion hotspots, third-party interference, and wax deposition — hours before catastrophic failure. A GIS-integrated dashboard maps every fault event to its GPS coordinate on the Trans-Niger Pipeline corridor in real time.
A joint initiative with Muhammed Fikri (PhD Researcher, Cranfield University) — building an end-to-end AI platform that transforms MRO parts management from reactive stockpiling to predictive, zero-AOG inventory control. The system ingests flight cycles, component MTBF curves, supplier lead times, and live maintenance records to produce a time-series demand forecast for every line-replaceable unit, triggering purchase orders autonomously before a stock-out can ground an aircraft.
🎓 Joint research with Muhammed Fikri, PhD Researcher · Cranfield University, UK
A vibration-signature-driven Digital Twin architecture for rotating equipment — aircraft engines, APU shafts, and gearboxes. Combines FFT/envelope analysis with a CNN-LSTM fault classifier trained on CWRU bearing datasets to predict spall, pitting, and cage fracture up to 50 duty cycles before detectable wear. Integrates MATLAB Simulink multi-body dynamics with a real-time Python inference layer for continuous RUL tracking under variable load and speed conditions.
A full-aircraft structural health monitoring Digital Twin combining Finite Element Analysis (ANSYS Mechanical) with Physics-Informed Neural Networks to track fatigue crack propagation, composite delamination, and buckling risk across the primary load-bearing structure. The FEM mesh is updated continuously from a distributed Structural Health Monitoring (SHM) network of piezoelectric Lamb-wave sensors and strain gauges, enabling real-time stress mapping and remaining fatigue life prediction compliant with EASA AMC 25.571.
A semantic "reasoning brain" for aircraft maintenance, encoding the full ATA iSpec 2200 chapter structure as an OWL/RDF knowledge graph. The system uses SPARQL inference to autonomously cross-reference airworthiness directives, task card dependencies, and MEL items — eliminating manual cross-referencing errors. A natural language interface (LLM + SPARQL backend) lets engineers query the maintenance program in plain English: "What deferred defects affect my dispatch today?"
The backbone powering every project in this logbook — a production-grade data engineering pipeline from simulation to insight. FlightGear telemetry arrives via UDP, is parsed in Python, streamed through Telegraf to InfluxDB 3 Core (time-series) and PostgreSQL (warehouse), visualised live in Grafana, and served via FastAPI microservices. MATLAB ODE engines feed PyTorch training pipelines; models are versioned in MLflow and deployed as REST endpoints. The full stack runs on a single Intel i7-12700H workstation — proving that enterprise-grade aerospace AI needs no cloud.
Real-time structural health monitoring dashboard with live telemetry visualization and predictive analytics.
Predictive maintenance planning tool for calculating optimized aircraft inspection intervals based on usage data.
Aviation safety monitoring interface for tracking incident trends and airworthiness compliance metrics.
Real-time telemetry bridge connecting FlightGear to cloud databases — UDP parsing, data validation, streaming to InfluxDB.
View RepositoryComplete ETL pipeline from simulation to visualization. Python UDP listeners, Telegraf, PostgreSQL/InfluxDB dual-database architecture.
Explore CodeFEA of aircraft wing spars using Ansys Workbench. Optimized material thickness for weight reduction while maintaining FAA safety factors.
Project CompletedCustom aviation dashboards — real-time flight parameters, system health indicators, predictive maintenance alerts.
View ScreenshotsDigital compilation of NCAA regulatory procedures — Technical Logs, CoA renewals, MEL protocols.
Read ExperienceDeep learning model for predicting landing gear failures from time-series sensor data. 94.3% accuracy with 50-cycle early warning.
View Model