Aeroengine Prognostics

Broad Category: Time Series + Text

Role: Principal Data Scientist

Duration: Jan 2018 – Jul 2019

Overview

Built prognostic models for aeroengine combustion distress and real-time diagnostics, improving reliability and engineering decision support.

Key Contributions

• Designed forecasting models for combustion distress using sensor and operational data.
• Enabled real-time event diagnosis and decision support for prognostics teams.
• Built text extraction tools for asset documents and scanned images.

Technology

Python, ML models, image-based text extraction, advanced analytics.

Outcome

Delivered a diagnostic and prognostic capability that supported early detection of engine issues and better maintenance planning.